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

Abstract

The embodiment of the invention provides an unmanned aerial vehicle collision avoidance method and device, a server, a medium and a product, and is applied to a satellite network server, the satellite network server receives flight information of a first unmanned aerial vehicle and a second unmanned aerial vehicle in multiple periods, and for any period, the flight information of the first unmanned aerial vehicle and the second unmanned aerial vehicle is acquired. And the satellite network server determines the minimum distance between the first unmanned aerial vehicle and the second unmanned aerial vehicle in the period according to the flight information of the first unmanned aerial vehicle and the flight information of the second unmanned aerial vehicle in the period, so that the minimum distance between the first unmanned aerial vehicle and the second unmanned aerial vehicle in each period is obtained. And according to the minimum distance between the first unmanned aerial vehicle and the second unmanned aerial vehicle corresponding to each period, predicting the cumulative number of collision times between the first unmanned aerial vehicle and the second unmanned aerial vehicle within the first preset duration, and further generating a detection and avoidance analysis result according to the cumulative number of collision times. And the first unmanned aerial vehicle and the second unmanned aerial vehicle perform collision avoidance according to the detection and avoidance analysis result. According to the invention, the transmission delay is reduced.

Description

Technical Field

[0001] The present application relates to drone technology, and in particular to a drone collision avoidance method, device, server, medium, and product. Background Art

[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, collisions between drones are likely to occur due to the increasing number of drones and the intersecting flight paths. Therefore, collision avoidance is necessary.

[0003] In related technologies, when avoiding collisions with drones, the drone to be avoided usually transmits the perception data between the drone to be avoided and other drones to the ground access gateway, which then processes the data through the core network to generate a detection and avoidance analysis (DAA) result, and then sends the DAA result to the drone to be avoided.

[0004] However, the above method has a high transmission delay. Summary of the Invention

[0005] The embodiments of the present application provide a collision avoidance method, device, server, medium and product for drones, which are used to achieve the technical effect of reducing transmission delay.

[0006] In a first aspect, an embodiment of the present application provides a collision avoidance method for a drone, which is applied to a satellite network server, wherein the satellite network server is disposed on a satellite, comprising:

[0007] Receiving flight information of multiple cycles sent by the first UAV and flight information of multiple cycles sent by the second UAV;

[0008] Determining a minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the flight information of the second UAV in each cycle;

[0009] Predicting a cumulative number of collisions within a first preset time period based on a minimum distance between the first UAV and the second UAV in each cycle, where the first preset time period is greater than time periods corresponding to the multiple cycles;

[0010] A detection and avoidance analysis result of the first UAV and the second UAV is generated according to the cumulative number of collisions, and the detection and avoidance analysis result is 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 flight information of the second UAV in each period includes:

[0012] For any period, determining, based on the flight information of the first UAV and the flight information of the second UAV during the period, the position information of the first UAV and the second UAV at each time instant predicted at intervals of a second preset time duration, where the second preset time duration is less than the first preset time duration;

[0013] Determining the distance between the first UAV and the second UAV at each moment according to the predicted position information of the first UAV and the second UAV at each moment;

[0014] According to the distance between the first UAV and the second UAV at each moment, the minimum distance between the first UAV and the second UAV in the period is determined.

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

[0016] predicting the number of collisions in each cycle based on the minimum distance between the first UAV and the second UAV in each cycle;

[0017] The number of collisions in each period within the first preset time period is accumulated to obtain a 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 drone and the second drone in each cycle includes:

[0019] For any period, if the minimum distance between the first UAV and the second UAV in the period is less than the preset safety distance threshold, the number of collisions is increased by 1 to obtain the predicted number of collisions in the period.

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

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

[0022] Determining a predicted target time corresponding to a minimum distance between the first UAV and the second UAV during the target period;

[0023] determining a collision count accumulation threshold value based on the predicted target time, the first preset duration, and the second preset duration;

[0024] Determining a target collision event type according to the cumulative number of collisions and the cumulative number of collisions threshold, where the collision event type includes a low-probability collision event and a high-probability collision event;

[0025] Generate detection and avoidance analysis results of the first UAV and the second UAV according to the target collision event type.

[0026] In a possible implementation, determining the target collision event type according to the cumulative number of collisions and the cumulative collision number threshold includes:

[0027] If the cumulative number of collisions is less than or equal to the cumulative number of collisions threshold, determining the target collision event type as the low-probability collision event;

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

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

[0030] Based on the new second preset time length, the steps of determining, for any period, the position information of the first UAV and the second UAV at each moment predicted every second preset time length according to the flight information of the first UAV and the flight information of the second UAV in the period are re-executed until the detection and avoidance analysis results of the first UAV and the second UAV are generated.

[0031] In a possible implementation, the method further includes:

[0032] generating warning information based on the detection and avoidance analysis results;

[0033] Sending the warning information to the first UAV and the second UAV; 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] In a second aspect, an embodiment of the present application provides a collision avoidance device for a drone, which is applied to a satellite network server, wherein the satellite network server is disposed on a satellite, and includes:

[0036] A receiving module, configured to receive flight information of multiple cycles sent by the first UAV and flight information of multiple cycles sent by the second UAV;

[0037] a determination module, configured to determine a minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the flight information of the second UAV in each cycle;

[0038] a prediction module, configured to predict a cumulative number of collisions within a first preset time period based on a minimum distance between the first UAV and the second UAV in each cycle, where the first preset time period is greater than a time period corresponding to the plurality of cycles;

[0039] and a processing module, configured to generate a detection and avoidance analysis result of the first UAV and the second UAV based on the cumulative number of collisions, and send the detection and avoidance analysis result to the first UAV and the second UAV for collision avoidance.

[0040] In a third aspect, an embodiment of the present application provides a satellite network server, comprising: a memory, a processor;

[0041] The memory stores computer-executable instructions;

[0042] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a satellite, comprising the satellite network server described in the third aspect.

[0044] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0045] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the first aspect and / or various possible implementation methods of the first aspect.

[0046] Embodiments of the present application provide a method, device, server, medium, and product for unmanned aerial vehicle (UAV) collision avoidance, which are applied to a satellite network server, which is located on a satellite and receives flight information from a first UAV and a second UAV over multiple cycles. For any cycle, the satellite network server determines the minimum distance between the first UAV and the second UAV during that cycle based on the flight information of the first UAV and the flight information of the second UAV during that cycle, thereby obtaining the minimum distance between the first UAV and the second UAV corresponding to each cycle. Based on the minimum distance between the first UAV and the second UAV corresponding to each cycle, the satellite network server predicts the cumulative number of collisions between the first UAV and the second UAV within a first preset time period, and generates a detection and avoidance analysis result based on the cumulative number of collisions, so that the first UAV and the second UAV can perform collision avoidance based on the detection and avoidance analysis result. According to the method of the present 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. There is no need for the perception data of the UAV to be avoided to be first transmitted to the ground access gateway and then transmitted to the core network in the related technology. The core network generates the detection and avoidance analysis results. This process is transmitted through the satellite network, which reduces the nodes passed through the ground network and thus reduces the transmission delay. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application;

[0049] Figure 2 A flowchart of a collision avoidance method for a drone provided in an embodiment of the present application;

[0050] Figure 3 A flowchart of a method for determining the minimum distance between a first drone and a second drone in each cycle provided by an embodiment of the present application;

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

[0052] Figure 5 A signaling flow chart of a collision avoidance method for a drone provided in an embodiment of the present application;

[0053] Figure 6A A signaling flow chart of another UAV collision avoidance method provided in an embodiment of the present application;

[0054] Figure 6B Another application scenario diagram provided in an embodiment of the present application;

[0055] Figure 7 A schematic structural diagram of a collision avoidance device for a drone provided in an embodiment of the present application;

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

[0057] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0058] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0059] In the description of the embodiments of the present application, terms such as "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is merely for the convenience of description and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present application.

[0060] In the description of the embodiments of this application, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0061] With the increasing 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, the simultaneous operation of multiple drones is becoming increasingly common. If drones cannot effectively avoid collisions, a collision will not only cause damage to the drones themselves, but also potentially cause harm to people and buildings on the ground. Therefore, collision avoidance between drones is crucial to ensuring flight safety.

[0062] In related technologies, collision avoidance between drones utilizes various sensors installed on drones. During flight, drones use these sensors to detect and obtain perception data about other drones. These sensors include, but are not limited to, conventional radar, lidar, cameras, and ultrasonic sensors. The perception data includes, but is not limited to, location, speed, and distance. After detecting this perception data, the drone transmits it to a ground access gateway, which then transmits it to the core network. The core network performs collision analysis based on the perception data, generating a Direct Assistance (DAA) result. This DAA result is then forwarded to the drone, enabling the drone to perform collision avoidance based on the DAA result.

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

[0064] Therefore, in response to the above-mentioned technical problems in the related art, the inventors discovered during their research that the reason for the high transmission delay is the long transmission process through the ground network. The perception data is first transmitted from the drone to the ground access gateway, and then transmitted to the core network through the ground access gateway, ultimately obtaining the DAA result. If the satellite network is used instead of the ground network, the above process can be omitted, and the drone's flight information can be directly obtained, and the DAA result can be generated based on the drone's flight information. Specifically, the first drone and the second drone send their corresponding flight information to the satellite network server at a predetermined time period. Therefore, the satellite network server can receive the flight information of the first drone for multiple cycles and the flight information of the second drone for multiple cycles, and determine the minimum distance between the two drones in each cycle based on the flight information in each cycle. Then, based on the minimum distance between the two drones in each cycle, the cumulative number of collisions within the first preset time period is predicted. Finally, a DAA result is generated based on the cumulative number of collisions and sent to the first and second drones to reduce transmission delay. Therefore, the present application proposes a collision avoidance method, device, server, medium, and product for drones.

[0065] To facilitate understanding of this application, the following is an explanation using exemplary application scenarios. Figure 1 , Figure 1 The present invention provides an application scenario diagram, which includes: a first drone 01, a second drone 02, a satellite network server 03, and a satellite 04. The satellite network server 03 is provided on the satellite 04.

[0066] The first drone 01 and the second drone 02 periodically transmit flight information to the satellite network server 03. For each period, the satellite network server 03 determines the minimum distance between the first drone 01 and the second drone 02 based on the flight information of the first drone 01 and the second drone 02 during that period, thereby obtaining the minimum distance between the two drones for each period. Based on the minimum distance between the two drones for each period, the satellite network server 03 predicts the cumulative number of collisions between the first drone 01 and the second drone 02 within a first preset time period. Based on the cumulative number of collisions, the satellite network server 03 generates a Direct Air Assistance (DAA) result and transmits the DAA result to the first drone 01 and the second drone 02, enabling the first drone 01 and the second drone 02 to perform collision avoidance based on the DAA result.

[0067] It can be understood that the above application scenarios are only used for illustration. This application does not limit the types, roles and functions of the first drone 01, the second drone 02, the satellite network server 03 and the satellite 04, which can be determined based on actual application conditions.

[0068] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0069] See Figure 2 , Figure 2 A schematic flow chart of a collision avoidance method for a drone provided in an embodiment of the present application is provided. The execution subject of the method may be a satellite network server, which is set on a satellite, such as Figure 2 As shown, the method includes:

[0070] S201: Receive flight information of multiple periods sent by a first UAV and flight information of multiple periods sent by a second UAV.

[0071] In this embodiment, the first drone and the second drone are any two drones.

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

[0073] Optionally,

[0074] The flight information can be shown 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 pre-specified by the user. The content in Table 1 above is for illustration only and does not limit this application.

[0078] Since the first UAV and the second UAV 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 and the flight information of the second UAV in each period.

[0079] S202: 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 flight information of the second UAV in each cycle.

[0080] After receiving the flight information corresponding to each period sent by the drone, the satellite network server predicts the drone's location information at multiple moments in the future based on the flight information corresponding to any period.

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

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

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

[0084] Thus, according to the above method, the minimum distance between the first UAV and the second UAV in each cycle is determined.

[0085] Optionally, the amount of position information of the first UAV predicted by the satellite network server in each period is the same as the amount of position information of the second UAV predicted.

[0086] S203: 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 cycle.

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

[0088] The satellite network server determines the specific number of cycles included in the first preset time length according to the time length corresponding to the cycle, the sending order of each cycle and the first preset time length.

[0089] The cumulative number of collisions within the first preset time period is predicted based on the relationship between the minimum distance between the first UAV and the second UAV corresponding to each period and the preset safety distance threshold.

[0090] S204: Generate detection and avoidance analysis results of 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.

[0091] The satellite network server determines the collision event type based on the cumulative number of collisions, where the collision event type includes a low-probability collision event and a high-probability collision event, and generates a DAA result for the first UAV and the second UAV based on the target collision event type.

[0092] After generating the DAA result, the satellite network server may directly send the DAA result to the first UAV and the second UAV.

[0093] After receiving the DAA result, the first UAV and the second UAV avoid collision by adjusting speed, changing course or changing altitude.

[0094] Optionally, after the satellite network server generates the DAA result, it can also first send the DAA result to the first drone controller that controls the first drone and the second drone controller that controls the second drone, and then send the DAA result to the first drone through the first drone controller, and send the DAA result to the second drone through the second drone controller.

[0095] Alternatively, the satellite network server may generate a warning message based on the DAA result and send the warning message directly to the first drone and the second drone. Alternatively, the warning message may be 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 message is sent to the first drone via the first drone controller and to the second drone via the second drone controller.

[0096] The content of the warning information may be as shown in Table 2:

[0097] Table 2

[0098]

[0099] The information that must be included in the warning information can be pre-specified by the user, but must at least include the type of collision event. The content in Table 2 above is only for illustration and does not limit this application.

[0100] In the above-described embodiment of the present application, a satellite network server receives flight information from a first drone and a second drone over multiple cycles. For any cycle, the satellite network server determines the minimum distance between the first and second drones during that cycle based on the flight information of the first and second drones, thereby obtaining the minimum distance between the first and second drones corresponding to each cycle. Based on the minimum distance between the first and second drones corresponding to each cycle, the satellite network server predicts the cumulative number of collisions between the first and second drones over 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 perform collision avoidance based on the detection and avoidance analysis result. Due to the wide coverage of the satellite network, the method of the present application can more quickly and directly obtain flight information from the first and second drones and generate a detection and avoidance analysis result based on the flight information. This eliminates the need for the related art process of first transmitting the sensing data of the drone to be avoided to a ground access gateway and then to the core network, where the detection and avoidance analysis result is generated by the core network. Transmission is performed via the satellite network, reducing the number of nodes traversed through the ground network and thus reducing transmission latency.

[0101] Furthermore, based on the above embodiment, the following embodiment illustrates a 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 flight information of the second UAV in each cycle.

[0102] See Figure 3 , Figure 3A flowchart of a method for determining the minimum distance between a first drone and a second drone in each cycle provided in an embodiment of the present application is provided. The method includes the following steps:

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

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

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

[0106] Related technologies also utilize a direct communication interface (PC5 interface) between drones, periodically broadcasting signals through the PC5 interface to avoid collisions between drones. However, this periodic broadcasting consumes a lot of drone power and is inflexible. More importantly, because the PC5 interface is a short-range communication interface, it is limited by transmit power and receive sensitivity, making it incapable of long-distance transmission. Therefore, a smaller broadcast time step, typically 1 or 2 seconds, is typically required to ensure a better broadcast signal, allowing other nearby drones to receive the broadcast signal and perform collision avoidance based on it.

[0107] In this application, the second preset time length used in predicting location information is It can be set flexibly. For example, in the case of a UAV with a faster flight speed for low-orbit satellite applications, For 10s-30s, a relatively long distance can be predicted. For example, in the application scenario of transport drones under the coverage of medium and high orbit satellites, by setting If the prediction time is 5-10 minutes, a longer distance can be predicted, thereby realizing collision avoidance of drones at a longer distance.

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

[0109] For example, taking the first cycle as an example, that is, taking the first time that the first UAV and the second UAV send their respective flight information to the satellite network server, assuming that the current velocity vector of the first UAV A is , the current velocity vector of the second UAV B is , the current position of the first UAV A is , the current position of the second drone B is , the satellite network server uses a preset artificial intelligence algorithm, such as a prediction algorithm, to send a signal every second preset time. The predicted position information of the first drone A and the second drone B at each moment is as follows:

[0110] First UAV A:

[0111] The first position information of the first moment of prediction is: ;

[0112] The second position information at the second moment of prediction is: ;

[0113] The predicted nth position information at the nth moment is: ;

[0114] Second UAV B:

[0115] The first position information of the first moment of prediction is: ;

[0116] The second position information at the second moment of prediction is: ;

[0117] The predicted nth position information at the nth moment is: ;

[0118] S302: Determine the distance between the first UAV and the second UAV at each moment based on the predicted position information of the first UAV and the second UAV at each moment.

[0119] Taking the first cycle in the above example as an example, the satellite network server determines the distance between the first UAV and the second UAV at each moment based on the predicted position information of the first UAV and the second UAV at each moment as follows:

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

[0121] The distance between the first UAV A and the second UAV B at the second moment is determined to be , and so on, the distance between the first drone A and the second drone B at the nth moment is determined to be .

[0122] S303: Determine the minimum distance between the first UAV and the second UAV in the cycle according to the distance between the first UAV and the second UAV at each moment.

[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 size of the distance to determine 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 drone and the second drone in each period included in the first preset time period.

[0125] In the above-described embodiment of the present application, for any period, the satellite network server determines the position information of the first and second drones at each moment predicted at intervals of a second preset time period based on the flight information of the first and second drones during the period, determines the distance between the first and second drones at each moment based on the predicted position information of the first and second drones, and then determines the minimum distance between the first and second drones during the period based on the distance between the first and second drones at each moment. The method of this embodiment predicts the position information at multiple moments based on the flight information of the first and second drones, and the minimum distance between the first and second drones obtained based on the predicted position information is more accurate.

[0126] Furthermore, based on the above embodiment, the satellite network server determines the minimum distance between the first UAV and the second UAV in each period included in the first preset time period, and then predicts the number of collisions in each period based on the minimum distance between the first UAV and the second UAV in each period.

[0127] Optionally, for any period, if the minimum distance between the first UAV and the second UAV in the period is less than or equal to the preset safety distance threshold , it is determined that the first UAV and the second UAV are likely to collide, and the number of collisions is increased by 1. If the minimum distance between the first UAV and the second UAV in this cycle is greater than the preset safety distance threshold, it is determined that the first UAV and the second UAV are unlikely to collide, and the number of collisions is not increased by 1, or it can be understood that the number of collisions is increased by 0, thereby obtaining the predicted number of collisions in this cycle. Indicates the preset distance range formed with point A of the first drone as the center. Indicates the preset distance range formed by the second drone's point B as the center.

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

[0129] Then, the satellite network server generates a detection and avoidance analysis result of the first UAV and the second UAV based on the cumulative number of collisions. Figure 4 A flow chart of a method for generating a detection and avoidance analysis result of a first UAV and a second UAV provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method includes the following steps:

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

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

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

[0133] According to the above method, the period corresponding to when the cumulative number of collisions is 1 is determined as the target period.

[0134] S402: Determine a predicted target time corresponding to the minimum distance between the first UAV and the second UAV in the target period.

[0135] For example, assuming that 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, assuming that the moment corresponding to the minimum distance is the second moment, the second moment is the target moment.

[0136] S403: Determine a collision count accumulation threshold according to the predicted target time, the first preset time duration, and the second preset time duration.

[0137] The collision count cumulative threshold is determined according to the preset formula below.

[0138]

[0139] in, Indicates the target moment. Taking the above example as an example, .

[0140] The predicted target time, the first preset time duration, and the second preset time duration are substituted into the above formula to determine the collision number cumulative threshold.

[0141] S404: Determine the target collision event type according to the cumulative number of collisions and the cumulative threshold of the number of collisions.

[0142] Among them, the collision event types include low-probability collision events and high-probability collision events. High-probability collision events indicate that there is a high probability of collision between drones, while low-probability collision events indicate that there is a low probability of collision between drones.

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

[0144] S405: Generate detection and avoidance analysis results of the first UAV and the second UAV according to the target collision event type.

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

[0146] In the above-described embodiment of the present application, the satellite network server determines the target period corresponding to the cumulative number of collisions being 1, determines the predicted target time corresponding to the minimum distance based on the minimum distance between the first and second drones during the target period, and determines the cumulative number of collisions threshold based on the predicted target time, the first preset duration, and the second preset duration. Furthermore, based on the cumulative number of collisions and the cumulative number of collisions threshold, the target collision event type is determined, and ultimately a detection and avoidance analysis result is generated. The method of this embodiment, by determining the cumulative number of collisions threshold and determining the target collision event type based on the cumulative number of collisions and the cumulative number of collisions 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 the present application, if the target collision event type determined is a high-probability collision event, the second preset time length is reduced to obtain a new second preset time length, and based on the new second preset time length, the step of re-executing from any period, according to the flight information of the first UAV and the flight information of the second UAV in the period, determining the position information of the first UAV and the second UAV at each moment predicted every second preset time length, 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 that of the above embodiment. Please refer to the above embodiment. To avoid redundancy, the description will not be repeated.

[0149] In the present application, by reducing the second preset time length, a new second preset time length is obtained, and the detection and avoidance analysis results of the first drone and the second drone are obtained again based on the new second preset time length, so that the obtained detection and avoidance analysis results are more accurate, thereby making the collision avoidance performed by the drone based on the accurate detection and avoidance analysis results more accurate.

[0150] In order to better understand this application, the following examples are briefly explained. Figure 5 , Figure 5 The signaling flow chart of a collision avoidance method for a drone provided in an embodiment of the present application includes the following steps:

[0151] S501: A first UAV sends a first request message to an Unmanned Aircraft System-Network Function (UAS-NF).

[0152] The first request message includes but is not limited to identification information and credential information of the first drone. 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.

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

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

[0155] S503. The UAS-NF authenticates and authorizes the first request message.

[0156] The UAS-NF verifies the identification information in the first request message to confirm whether its format or encoding complies with 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 the UAS-NF confirms that the identity of the first UAV is valid, it authorizes the preset operations that can be performed by the first UAV.

[0158] S504. The UAS-NF authenticates and authorizes the second request message.

[0159] The way in which the UAS-NF authenticates and authorizes the second request message is the same as step S503 and will not be repeated here.

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

[0161] S505: The satellite network server sends a notification request for subscribing to the first UAV and the second UAV to the UAS-NF.

[0162] The satellite network server may also be referred to as a collision avoidance application server. The collision avoidance application server periodically or cyclically sends a notification request for subscribing to the first UAV and the second UAV to the UAS-NF.

[0163] S506. The UAS-NF sends a subscription permission notification message to the satellite network server.

[0164] After receiving the notification request sent by the satellite network server, the UAS-NF sends a subscription permission notification message to the satellite network server because the authentication of the first UAV and the second UAV is successful, so that the satellite network server can receive the flight information sent by the first UAV and the second UAV.

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

[0166] The first UAV periodically sends 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 the 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 a detection and avoidance analysis result based on the flight information sent by the first UAV and the second UAV.

[0170] The satellite network server analyzes the flight information transmitted by the first UAV over multiple periods and the flight information transmitted by the second UAV over multiple periods using a preset artificial intelligence algorithm, and generates a DAA result. For specific implementation methods, please refer to the above-mentioned embodiments.

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

[0172] After receiving the DAA result, the first UAV performs collision avoidance according to the DAA result.

[0173] S511. The 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 according to the DAA result.

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

[0176] See Figure 6A , Figure 6A A signaling flow chart of another UAV collision avoidance method provided in an embodiment of the present application is provided. Figure 6B Another application scenario diagram provided in the embodiment of the present application 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 set on the satellite 04. Figure 6B ,right Figure 6A The signaling flow chart in FIG. 1 may include the following steps:

[0177] S601. The first UAS 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. 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.

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

[0180] The second request message includes but is not limited to identification information and credential information of the first drone. 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.

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

[0182] The third request message includes but is not limited to 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 UAS sends a fourth request message to the UAS-NF.

[0184] The fourth request message includes but is not limited to identification information and credential information of the second drone. 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.

[0185] S605. The UAS-NF authenticates and authorizes the corresponding UAV or UAV controller according to 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 embodiment, and will not be repeated to avoid redundancy.

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

[0188] S606: The satellite network server sends a notification request for subscribing to the first UAV and the second UAV to the UAS-NF.

[0189] S607: The UAS-NF sends a subscription permission notification message to the satellite network server.

[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] S610: The satellite network server generates warning information according to the flight information sent by the first UAV and the second UAV.

[0193] The satellite network server analyzes the flight information from multiple cycles sent by the first UAV and the flight information from multiple cycles sent by the second UAV using a preset artificial intelligence algorithm, generating a DAA result. The server also generates a warning message based on the DAA result. The warning message includes the following information:

[0194] S611. The satellite network server sends the warning information to the first UAV controller.

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

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

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

[0198] S614: The first UAV controller sends the avoidance strategy to the first UAV.

[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 warning information.

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

[0202] S616. The second UAV controller sends the avoidance strategy to the second UAV.

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

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

[0205] In the above-mentioned embodiment of the present application, the satellite network server can 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 related art to transmit the perception data of the drone detection to be avoided to the ground access gateway and then to the core network. The core network generates the detection and avoidance analysis results. This process is transmitted via the satellite network, reducing the number of nodes transmitted through the ground network and thus reducing transmission latency. In addition, in the present application, compared with the related art that only avoids collisions of one drone based on the application layer mechanism of a single drone, that is, only avoiding collisions of one drone based on the information detected by one drone, the present application can perform collision avoidance on multiple drones based on the flight information of multiple drones, thus making the method of the present application more applicable.

[0206] Figure 7 A schematic diagram of the structure of a collision avoidance device for a UAV provided in an embodiment of the present application is applied to a satellite network server, which is arranged on a satellite, such as Figure 7 Shown, including:

[0207] The receiving module 701 is configured to receive flight information of multiple periods sent by the first UAV and flight information of multiple periods sent by the second UAV.

[0208] The determination module 702 is configured 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 flight information of the second UAV in each cycle.

[0209] The prediction module 703 is configured 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, where the first preset time period is greater than the time periods corresponding to the multiple cycles.

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

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

[0212] For any period, the position information of the first UAV and the second UAV at each moment predicted every second preset time period is determined based on the flight information of the first UAV and the flight information of the second UAV in the period, and the second preset time period is less than the first preset time period.

[0213] The distance between the first UAV and the second UAV at each moment is determined according to the predicted position information of the first UAV and the second UAV at each moment.

[0214] According to the distance between the first UAV and the second UAV at each moment, the minimum distance between the first UAV and the second UAV in the period is determined.

[0215] In one possible implementation, the prediction module 703 is specifically configured to:

[0216] According to the minimum distance between the first UAV and the second UAV in each cycle, the number of collisions in each cycle is predicted.

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

[0218] In one possible implementation, the prediction module 703 is specifically configured to:

[0219] For any period, if the minimum distance between the first UAV and the second UAV in the period is less than or equal to the preset safety distance threshold, the number of collisions is increased by 1 to obtain the predicted number of collisions in the period.

[0220] In one possible implementation, the processing module 704 is specifically configured to:

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

[0222] According to the minimum distance between the first UAV and the second UAV in the target period, a predicted target time corresponding to the minimum distance is determined.

[0223] A collision count accumulation threshold is determined based on the predicted target time, the first preset time duration, and the second preset time duration.

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

[0225] Generate detection and avoidance analysis results for the first UAV and the second UAV based on the target collision event type.

[0226] In one possible implementation, the processing module 704 is specifically configured to:

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

[0229] In a possible implementation, the processing module 704 is further configured to:

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

[0231] Based on the new second preset time length, the steps of determining the position information of the first UAV and the second UAV at each moment predicted every second preset time length for any period according to the flight information of the first UAV and the flight information of the second UAV in the period are re-executed until the detection and avoidance analysis results of the first UAV and the second UAV are generated.

[0232] In a possible implementation, the processing module 704 is further configured to:

[0233] Generate early warning information based on the detection and avoidance analysis results.

[0234] Send the warning information to the first UAV and the second UAV. Or,

[0235] 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.

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

[0237] The present application also provides a satellite, which includes the satellite network server mentioned in any of the above embodiments. It is understood that the type and function of the satellite are not limited in the present application.

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

[0239] During the specific implementation process, at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that the at least one processor 801 performs the above method.

[0240] The specific implementation process of the processor 801 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0241] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or 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.

[0243] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0244] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0245] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0246] The readable storage medium may be implemented by any type of volatile or non-volatile memory 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 may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0247] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0248] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0249] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0250] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may 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 the present invention, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0252] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with 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. 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 those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A collision avoidance method for an unmanned aerial vehicle, characterized in that: Applied to a satellite network server, the satellite network server being disposed on a satellite, the method comprising: Receiving flight information of multiple cycles sent by the first UAV and flight information of multiple cycles sent by the second UAV; Determining a minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the flight information of the second UAV in each cycle; Predicting a cumulative number of collisions within a first preset time period based on a minimum distance between the first UAV and the second UAV in each cycle, where the first preset time period is greater than time periods corresponding to the multiple cycles; A detection and avoidance analysis result of the first UAV and the second UAV is generated according to the cumulative number of collisions, and the detection and avoidance analysis result is sent to the first UAV and the second UAV for collision avoidance.

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

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

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

5. The method according to claim 2, characterized in that Generating a detection and avoidance analysis result of the first UAV and the second UAV based on the cumulative number of collisions includes: Determine the target period corresponding to when the cumulative number of collisions is 1; Determining a predicted target time corresponding to a minimum distance between the first UAV and the second UAV during the target period; determining a collision count accumulation threshold value based on the predicted target time, the first preset duration, and the second preset duration; Determining a target collision event type according to the cumulative number of collisions and the cumulative number of collisions threshold, where the collision event type includes a low-probability collision event and a high-probability collision event; Generate detection and avoidance analysis results of the first UAV and the second UAV according to the target collision event type.

6. The method according to claim 5, characterized in that The determining the target collision event type according to the cumulative number of collisions and the cumulative number of collisions threshold includes: If the cumulative number of collisions is less than the cumulative number of collisions threshold, determining the target collision event type as the low-probability collision event; If the cumulative number of collisions is greater than or equal to the cumulative number of collisions threshold, 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, reducing the second preset time length to obtain a new second preset time length; Based on the new second preset time length, the steps of determining, for any period, the position information of the first UAV and the second UAV at each moment predicted every second preset time length according to the flight information of the first UAV and the flight information of the second UAV in the period are re-executed 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 to 7, characterized in that Also includes: generating warning information based on the detection and avoidance analysis results; Sending the warning information to the first UAV and the second UAV; 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 a drone, characterized in that: Applicable to a satellite network server, the satellite network server is arranged on a satellite, and includes: A receiving module, configured to receive flight information of multiple cycles sent by the first UAV and flight information of multiple cycles sent by the second UAV; a determination module, configured to determine a minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the flight information of the second UAV in each cycle; a prediction module, configured to predict a cumulative number of collisions within a first preset time period based on a minimum distance between the first UAV and the second UAV in each cycle, where the first preset time period is greater than a time period corresponding to the plurality of cycles; and a processing module, configured to generate a detection and avoidance analysis result of the first UAV and the second UAV based on the cumulative number of collisions, and send the detection and avoidance analysis result 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-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the collision avoidance method for the drone according to any one of claims 1 to 8.

11. A satellite, characterized in that: Comprising the satellite network server as claimed 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 a drone according to any one of claims 1 to 8.

13. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the collision avoidance method for a drone according to any one of claims 1 to 8.

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