Communication method and apparatus of unmanned aerial vehicle, device, medium, program product

Abstract

The present disclosure provides a communication method and device of a UAV, equipment, a medium, and a program product. The method comprises: S1, determining real-time boundary parameters of a non-transmission zone and issuing the real-time boundary parameters of the non-transmission zone to each slave UAV in at least one slave UAV. S2, sending a data request frame to each slave UAV in at least one adjacent slave UAV. S3, receiving neighbor awareness data from each slave UAV in at least one adjacent slave UAV. S4, calculating a coordination consistency score. S5, generating a coordination verification result instruction and issuing the coordination verification result instruction to the first slave UAV. According to the embodiments of the present disclosure, multi-UAV coordination awareness and consistency quantification verification can be achieved, the area misjudgment caused by single-point positioning error can be greatly reduced, high-reliable and differentiated cluster coordination determination can be achieved, and an accurate decision basis can be provided for subsequent interference suppression and real-time transmission of key data.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology for unmanned aerial vehicles (UAVs), and particularly to a communication method and apparatus for UAVs, an electronic device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] Drones are widely used in aerial photography, agriculture, logistics, and other fields. When flying at high altitudes, drones typically form a line-of-sight (LOS) propagation path with ground base stations, resulting in minimal transmission loss and wide uplink signal coverage. However, in densely populated areas like cities, when drones transmit uplink signals to a target base station, the use of omnidirectional or poorly directional antennas can easily interfere with neighboring base stations. Furthermore, at disaster sites where medical and communication equipment is present, signals transmitted by drones to base stations can easily "cross-channel," interfering with nearby base stations or critical ground equipment, disrupting normal operations. Therefore, how to ensure the communication reporting capabilities of drones while suppressing uplink signal interference with other equipment has become a pressing issue.

[0003] Some existing solutions use fixed geographical non-transmission zones, which cannot adapt to temporary interference sources (such as emergency communication vehicles or temporary airspace control areas), easily leading to unsuppressed or oversuppressed interference. For example, new interference sources such as medical equipment at disaster sites cannot be covered in time by fixed non-transmission zones and will still be affected by uplink signals from drones. After the temporary control ends, communication is still restricted in fixed non-transmission zones, reducing efficiency.

[0004] Current technology only reports data after the drone leaves the non-transmission area, which cannot meet the needs of emergency scenarios. If the drone experiences battery failure (such as a sudden drop in power to 15%) or detects an illegal interference source in the non-transmission area, the delayed reporting will prevent the base station from dispatching rescue in a timely manner, increasing the risk of crash.

[0005] Furthermore, existing solutions rely on individual drones making independent judgments, failing to consider cluster scenarios. When multiple drones simultaneously enter non-transmission zones, individual drone perception errors (such as GNSS positioning deviations of ±3 meters) can lead to some drones misjudging the area. Moreover, simultaneous reporting after leaving can easily cause channel congestion, reducing transmission success rates. Continuous real-time position and status assessments by drones (such as 10 GNSS positioning checks per second) will shorten flight time, especially in scenarios requiring long-endurance flight, such as low-altitude logistics, severely impacting mission completion. Summary of the Invention

[0006] This disclosure provides a communication method, apparatus, device, medium, and program product for unmanned aerial vehicles (UAVs).

[0007] In a first aspect, this disclosure provides a communication method for a drone, applied to a navigation drone. The communication method includes: S1, determining real-time boundary parameters of a non-transmission zone and sending these parameters to each of at least one slave drone. S2, in response to receiving a pending confirmation signal from a first slave drone, sending a data request frame to each of at least one neighboring slave drone, so that each of the at least one neighboring slave drone senses the position of the first slave drone based on the data request frame and reports neighbor sensing data. The first slave drone is one of the at least one slave drone. The pending confirmation signal is issued by the first slave drone after N consecutive local determinations indicating that it is located within a non-transmission zone. The local determination result is obtained by the first slave drone after acquiring its own state sensing data, based on the real-time boundary parameters of the non-transmission zone and the state sensing data, determining whether it is located within a dynamic non-transmission zone. The state sensing data includes at least position data and time data. The pending confirmation signal includes the local determination result and the state sensing data. N is a positive integer. Each of the at least one neighboring drones is a drone that is geographically adjacent to the first drone. Neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local position determination results of each of the at least one neighboring drones. S3: Receive neighbor perception data from each of the at least one neighboring drones. S4: Calculate a cooperative consistency score based on the signal to be confirmed and the neighbor perception data from each of the at least one neighboring drones. S5: Compare the cooperative consistency score with a preset threshold, generate a cooperative verification result instruction, and send the cooperative verification result instruction to the first drone, so that the first drone determines its final regional affiliation status according to the cooperative verification result instruction, and transmits data according to the final regional affiliation status using a hierarchical dynamic transmission mechanism.

[0008] In some embodiments, S1 includes: S11, collecting environmental perception data and data from third-party systems during flight. The environmental perception data includes the position data of the navigation drone, environmental interference signal strength data, and the attitude data of the navigation drone. The data from third-party systems includes emergency management data, traffic control data, and power grid electromagnetic interference data. S12, fusing the environmental perception data and the data from third-party systems to generate initial boundary parameters for the non-transmission zone. These boundary parameters include the effective start time of the planar boundary, the altitude boundary, and the effective start time and failure time of the time boundary. S13, updating the initial boundary parameters of the non-transmission zone in real time to determine the real-time boundary parameters of the non-transmission zone.

[0009] In some embodiments, in S11, the method for the pilot drone to collect environmental perception data includes: S111, acquiring the position data of the pilot drone using the built-in Global Navigation Satellite System (GNSS) module; S112, acquiring environmental interference signal strength data using the built-in radio frequency sensor. Environmental interference signals include: emergency equipment signals and nearby base station signals; S113, acquiring the attitude data of the pilot drone using the built-in Inertial Measurement Unit (IMU) module.

[0010] In some embodiments, S12 includes: S121, based on the improved DS evidence theory, using the position data of the navigation drone and the data of the third-party system as the first evidence body, the environmental interference signal strength data as the second evidence body, and the attitude data of the navigation drone as the third evidence body, forming three evidence bodies. A basic probability allocation function on the recognition framework is assigned to each of the three evidence bodies. Here, A represents that the spatial grid cell belongs to the non-transmission area, B represents that the spatial grid cell does not belong to the non-transmission area, and represents that it is uncertain whether the spatial grid cell belongs to the non-transmission area. The spatial grid cell is related to the position data of the drone in the first evidence body. S122, the historical error rate of each of the three evidence bodies is obtained, and the credibility weight of each of the three evidence bodies is calculated based on the historical error rate of each of the three evidence bodies. S123, the conflict coefficients of pairwise evidence bodies in the three evidence bodies are detected, and in the case of serious conflict, the basic probability allocation function of each of the three evidence bodies is corrected according to the credibility weight of each of the three evidence bodies, resulting in the corrected basic probability allocation function of each of the three evidence bodies. S124: Based on the credibility weights of each of the three evidence bodies, the modified basic probability allocation functions of each evidence body are weighted and averaged to obtain the weighted average basic probability allocation function of the three evidence bodies. The DS combination rule is then applied to iteratively fuse the weighted average basic probability allocation function of the three evidence bodies to obtain the fusion probability of each spatial grid cell. S125: Based on the fusion probability of each spatial grid cell, the region division result of the spatial grid cell is determined, and the initial boundary parameters of the non-transmission zone are generated based on the region division result of the spatial grid cell. The region division result of the spatial grid cell includes whether the spatial grid cell belongs to the non-transmission zone, whether the spatial grid cell does not belong to the non-transmission zone, and whether the spatial grid cell is uncertain about whether it belongs to the non-transmission zone.

[0011] In some embodiments, step S126 is also included. Step S126: If the spatial grid cell region division result is uncertain whether the spatial grid cell belongs to the non-transmission area, the navigation UAV increases the collection frequency of environmental perception data and third-party system data, and re-executes steps S121 to S125.

[0012] In some embodiments, in S125, generating initial non-transmission zone boundary parameters based on the spatial grid cell region division results includes: S1251, marking spatial grid cells belonging to the non-transmission zone as valid non-transmission zones. S1252, obtaining the controlled zone plane boundary based on third-party system data, using the union of the valid non-transmission zone and the controlled zone plane boundary as the initial vertex set, and generating convex polygons as plane boundaries using the Graham scan method. S1253, obtaining the controlled zone height range data based on third-party system data, using the union of the valid non-transmission zone and the controlled zone height range data as the base height range, and generating height boundaries according to a preset dynamic interference threshold. The preset dynamic interference threshold is dynamically set based on height layering and the scene of the valid non-transmission zone. S1254, determining the earliest value of the effective time and the latest value of the failure time based on third-party system data, using the earliest value of the effective time as the effective start time of the time boundary, and using the latest value of the failure time as the failure time of the time boundary. S1255 generates the initial non-transmission zone boundary parameters based on the effective start time of the planar boundary, height boundary, time boundary, and the failure time of the time boundary.

[0013] In some embodiments, S13 includes: in response to the occurrence of any one of at least one preset event, updating the boundary parameters of the initial non-transmission region in real time to determine the real-time boundary parameters of the non-transmission region. The at least one preset event includes: the appearance of a new interference source, a change in the location of an existing interference source, an update of data from a third-party system, and a decrease in the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission region.

[0014] In some embodiments, the boundary parameters of the initial non-transmission zone are updated in real time, including: S131, increasing the acquisition frequency of environmental sensing data and data from third-party systems, and performing data fusion based on the latest environmental sensing data and data from third-party systems obtained after increasing the acquisition frequency to generate updated boundary parameters of the non-transmission zone. S132, comparing the updated boundary parameters of the non-transmission zone with the initial boundary parameters of the non-transmission zone. If the comparison result meets the preset update conditions, the updated boundary parameters of the non-transmission zone are used as the real-time boundary parameters of the non-transmission zone; otherwise, the initial boundary parameters of the non-transmission zone are used as the real-time boundary parameters of the non-transmission zone. The preset update conditions include: the planar boundary overlap rate is less than 80%, the height range variation exceeds 20 meters, and the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission zone decreases by more than 0.1.

[0015] In some embodiments, S4 includes: S41, calculating a position consistency score and a height consistency score based on the local determination result of the first slave drone's position and the relative position observations of each slave drone among at least one neighboring slave drone to the first slave drone. S42, calculating the percentage of slave drones whose position determination results are located in non-transmission areas based on the local determination results of the positions of each slave drone among at least one neighboring slave drone, and calculating a determination consistency score based on the percentage. S43, weighted summing of the position consistency score, the height consistency score, and the determination consistency score to obtain a collaborative consistency score.

[0016] In some embodiments, the preset threshold includes a first preset threshold and a second preset threshold, and the collaborative verification result instruction includes a first collaborative verification result instruction, a second collaborative verification result instruction, and a third collaborative verification result instruction. S5 includes: if the collaborative consistency score is greater than or equal to the first preset threshold, generating a first collaborative verification result instruction. The first collaborative verification result instruction indicates that the first slave drone is located in a non-transmission area. If the collaborative consistency score is greater than or equal to the second preset threshold but less than the first preset threshold, generating a second collaborative verification result instruction instructing the first slave drone to increase its positioning sampling frequency and re-report the judgment result after a preset time. If the collaborative consistency score is less than the second preset threshold, generating a third collaborative verification result instruction. The third collaborative verification result instruction instructs the first slave drone to enable RTK differential positioning, recalculate its position, and re-report the judgment result.

[0017] In some embodiments, S5 further includes: in response to receiving data after the first drone re-reports the judgment result based on the second collaborative verification result instruction and the third collaborative verification result instruction, recalculating the collaborative consistency score; if the recalculated collaborative consistency score is still less than the second preset threshold, generating and reporting device abnormal information.

[0018] In some embodiments, the hierarchical dynamic transmission mechanism includes: for urgent data, regardless of whether the first slave drone is located in a non-transmission zone, interrupting the currently ongoing non-urgent transmission task and reporting the urgent data in real time via an ultra-wideband (UWB) link using frequency hopping technology. For important data, if the first slave drone is located in a non-transmission zone, the important data is cached in local storage; if the first slave drone is not located in a non-transmission zone, the cached important data is reported via a 5G link. For ordinary data, it is compressed and cached using a preset compression algorithm, and uploaded via a narrowband Internet of Things (NB-IoT) link during a preset low-power communication period.

[0019] In some embodiments, the method further includes: S6, obtaining the current flight scenario type, and determining the number of each slave drone in at least one adjacent slave drone and a preset threshold based on the current flight scenario type. Scenario types include densely populated urban areas, open areas, or emergency rescue areas.

[0020] Secondly, this disclosure provides a communication device for a drone, applied to a navigation drone. The communication device includes: a real-time boundary parameter determination module for a non-transmission zone, used to determine the real-time boundary parameters of the non-transmission zone and send these parameters to each of at least one slave drone. A data request frame sending module, used to send a data request frame to each of at least one neighboring slave drone in response to receiving a pending confirmation signal from a first slave drone, so that each of the at least one neighboring slave drone can perceive the position of the first slave drone based on the data request frame and report neighbor perception data. The first slave drone is one of the at least one slave drone. The pending confirmation signal is issued by the first slave drone after N consecutive local determinations indicating that the first slave drone is located within the non-transmission zone. The local determination result is obtained by the first slave drone after acquiring its own state perception data, based on the real-time boundary parameters of the non-transmission zone and the state perception data, determining whether it is located within a dynamic non-transmission zone. The state perception data includes at least position data and time data. The pending confirmation signal includes the local determination result and the state perception data. N is a positive integer. Each of the at least one neighboring drones is a drone that is geographically adjacent to the first drone. Neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local position determination results of each of the at least one neighboring drones. A data receiving module receives neighbor perception data from each of the at least one neighboring drones. A cooperative consistency score calculation module calculates a cooperative consistency score based on the signal to be confirmed and the neighbor perception data from each of the at least one neighboring drones. A cooperative verification result instruction generation module compares the cooperative consistency score with a preset threshold, generates a cooperative verification result instruction, and sends the instruction to the first drone, enabling the first drone to determine its final regional affiliation status based on the instruction and transmit data according to a hierarchical dynamic transmission mechanism based on the final regional affiliation status.

[0021] Thirdly, this disclosure provides a communication method for a UAV, applied to a first slave UAV. The communication method includes: A1, receiving real-time boundary parameters of the non-transmission zone from a pilot UAV; A2, acquiring the first slave UAV's own state perception data, which includes at least position data and time data; A3, determining whether the first slave UAV is located within a dynamic non-transmission zone based on the real-time boundary parameters of the non-transmission zone and the state perception data, obtaining a local determination result; A4, if the number of determinations indicating that the first slave UAV is located within the dynamic non-transmission zone is greater than N, sending a pending confirmation signal to the pilot UAV, causing the pilot UAV to send data request frames to each of at least one neighboring slave UAV, enabling each of the at least one neighboring slave UAV to perceive the position of the first slave UAV based on the data request frames, report neighbor perception data, calculate a cooperative consistency score based on the pending confirmation signal and the neighbor perception data from each of the at least one neighboring slave UAV, compare the cooperative consistency score with a preset threshold, generate a cooperative verification result instruction, and issue the cooperative verification result instruction to the first slave UAV. The pending confirmation signal includes the local determination result and the state perception data. N is a positive integer. Each of the at least one neighboring drones is a drone that is geographically adjacent to the first drone. Neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local position determination results of each of the at least one neighboring drones. A5 receives the collaborative verification result instruction from the lead drone, determines the final area affiliation status based on the instruction, and transmits data according to the hierarchical dynamic transmission mechanism based on the final area affiliation status.

[0022] Fourthly, this disclosure provides a communication device for a drone, applied to a first slave drone. The communication device includes: a boundary parameter receiving module for receiving real-time boundary parameters from a non-transmission zone of a pilot drone; a perception module for acquiring the first slave drone's own state perception data, which includes at least position data and time data; a local determination module for determining whether it is located within a dynamic non-transmission zone based on the real-time boundary parameters and state perception data of the non-transmission zone, and obtaining a local determination result; and a pending confirmation signal sending module for sending a pending confirmation signal to the pilot drone when the number of determinations indicating the first slave drone is located within the dynamic non-transmission zone is greater than N. This causes the pilot drone to send data request frames to each of at least one neighboring slave drone, enabling each of the at least one neighboring slave drone to perceive the position of the first slave drone based on the data request frames, report neighbor perception data, calculate a cooperative consistency score based on the pending confirmation signal and the neighbor perception data from each of the at least one neighboring slave drone, compare the cooperative consistency score with a preset threshold, generate a cooperative verification result instruction, and send the cooperative verification result instruction to the first slave drone. N is a positive integer. Each of the at least one neighboring drones is a drone that is geographically adjacent to the first drone. Neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local position determination results of each of the at least one neighboring drones. A collaborative verification result instruction receiving module receives collaborative verification result instructions from the lead drone, determines the final area affiliation status based on the instructions, and transmits data according to a hierarchical dynamic transmission mechanism based on the final area affiliation status.

[0023] Fifthly, this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the aforementioned communication method for a drone.

[0024] Sixthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described communication method for the unmanned aerial vehicle.

[0025] In a seventh aspect, this disclosure provides a computer program product comprising computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is executed in a processor of an electronic device, the processor in the electronic device performs the aforementioned communication method for a drone.

[0026] The embodiments provided in this disclosure can significantly reduce regional misjudgments caused by single-point positioning errors through multi-UAV collaborative perception and consistency quantification verification, achieve highly reliable and differentiated cluster collaborative judgment, and provide an accurate decision-making basis for subsequent interference suppression and real-time transmission of key data.

[0027] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the embodiments of the present disclosure to explain the disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the detailed description of exemplary embodiments with reference to the accompanying drawings, in which: Figure 1 A flowchart illustrating a communication method for an unmanned aerial vehicle (UAV) provided in this embodiment of the disclosure; Figure 2 A flowchart illustrating another communication method for a drone provided in this embodiment of the disclosure; Figure 3 A flowchart illustrating another communication method for a drone provided in this embodiment of the disclosure; Figure 4 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 5 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 6 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 7 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 8 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 9 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 10 A block diagram of a communication device for an unmanned aerial vehicle (UAV) provided in an embodiment of this disclosure; Figure 11 A flowchart illustrating yet another communication method for a drone provided in this disclosure embodiment; Figure 12 A block diagram of another communication device for a drone provided in an embodiment of this disclosure; Figure 13 This is a block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0030] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0031] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0033] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0034] Figure 1 A flowchart illustrating a communication method for a drone provided in this disclosure. This method is applied to a navigation drone, as shown below. Figure 1 The method includes: Step S1: Determine the real-time boundary parameters of the non-transmission zone and send the real-time boundary parameters of the non-transmission zone to each slave drone in at least one slave drone.

[0035] Step S2: In response to receiving the pending confirmation signal from the first slave drone, a data request frame is sent to each of the at least one neighboring slave drones, so that each of the at least one neighboring slave drones can perceive the position of the first slave drone according to the data request frame and report the neighbor perception data.

[0036] In step S2, the first slave drone is one of at least one slave drone. The confirmation signal is emitted by the first slave drone after N consecutive local determinations that it is located within the non-transmission zone. The local determination result is obtained by the first slave drone after acquiring its own state awareness data, based on the real-time boundary parameters of the non-transmission zone and the state awareness data, to determine whether it is located within the dynamic non-transmission zone. The state awareness data includes at least position data and time data. The confirmation signal includes the local determination result and the state awareness data. N is a positive integer. Each of the at least one neighboring slave drones is a slave drone that is positionally adjacent to the first slave drone. The neighbor awareness data includes the relative position observations and signal directions of each of the at least one neighboring slave drones relative to the first slave drone, as well as the local determination results of the position of each of the at least one neighboring slave drones.

[0037] Step S3: Receive neighbor perception data from each of the slave drones in at least one neighboring slave drone.

[0038] Step S4: Calculate the collaborative consistency score based on the signal to be confirmed and the neighbor perception data from each of the slave drones in at least one neighboring slave drone.

[0039] Step S5: Compare the collaborative consistency score with a preset threshold, generate a collaborative verification result instruction, and send the collaborative verification result instruction to the first slave drone so that the first slave drone can determine the final regional affiliation status according to the collaborative verification result instruction, and transmit data according to the final regional affiliation status in accordance with the hierarchical dynamic transmission mechanism.

[0040] According to embodiments of this disclosure, non-transmission zone boundaries are dynamically generated by a navigation drone, and multi-drone collaborative perception and consistency quantification verification significantly reduce regional misjudgments caused by single-point positioning errors, achieving highly reliable and differentiated cluster collaborative judgment, and providing an accurate decision-making basis for subsequent interference suppression and real-time transmission of key data.

[0041] In some embodiments, such as Figure 2 As shown, step S1 includes: Step S11: Collect environmental perception data and data from third-party systems during flight.

[0042] In step S11, the environmental perception data includes the location data of the navigation drone, the intensity data of environmental interference signals, and the attitude data of the navigation drone. Data from third-party systems includes emergency management data, traffic control data, and power grid electromagnetic interference data.

[0043] In some embodiments, such as Figure 3 As shown, in step S11, the method for the navigation drone to collect environmental perception data includes steps S111 to S113.

[0044] Step S111: Use the built-in Global Navigation Satellite System (GNSS) module to obtain the location data of the navigation drone.

[0045] Step S112: Collect environmental interference signal strength data using the built-in radio frequency sensor.

[0046] In step S112, the environmental interference signals include: emergency equipment signals and signals from nearby base stations.

[0047] Step S113: Use the built-in Inertial Measurement Unit (IMU) module to acquire the attitude data of the pilot UAV.

[0048] For example, the pilot drone is equipped with three types of high-precision sensors to collect data: ① GNSS module (accuracy ±0.5 meters) to obtain the real-time position of itself and other drones; ② radio frequency sensor to collect the strength of surrounding interference signals (such as emergency equipment signals, nearby base station signals); ③ IMU module (sampling rate 100Hz) to obtain drone attitude data.

[0049] For example, data from third-party systems can be obtained through base stations. Emergency management data may include temporary rescue information from the emergency management system, including latitude and longitude ranges, effective time, etc. Traffic control data may include temporary airspace control information from the traffic management system. Power grid electromagnetic interference data may include electromagnetic interference information from high-voltage lines of the power grid system.

[0050] For example, the ground base station accesses three types of third-party systems, obtains data from the third-party systems, and then sends it to the navigation drone at a certain preset frequency, or the navigation drone requests data from the third-party systems at a certain preset frequency.

[0051] Step S12: The environmental perception data and the data from the third-party system are fused to generate the initial boundary parameters of the non-transmission zone.

[0052] In step S12, the boundary parameters include the effective start time of the planar boundary, the height boundary, the time boundary, and the time boundary failure time.

[0053] Understandably, steps S12 and S13 can be executed by an edge computing node, which can be located inside the pilot drone or on the ground. This embodiment of the present disclosure uses an edge computing node located on the pilot drone as an example.

[0054] Understandably, the specific method of step S12 can be as follows: the edge computing node can uniformly convert the position data of the navigation UAV into the WGS84 coordinate system, map the radio frequency interference intensity to a spatial grid (10m × 10m grid cell), and record the maximum interference intensity of the area in each grid cell. Then, the improved DS evidence theory is used to process the multi-source data, and the improved DS evidence theory solves the traditional DS evidence conflict problem (such as GNSS showing that it is in the rescue area, but the radio frequency interference has not exceeded the threshold), realizes the reliable fusion of multi-source data, and finally generates the three-dimensional boundary of the dynamic non-transmission area.

[0055] In some embodiments, such as Figure 4 As shown, step S12 includes steps S121 to S125.

[0056] Step S121: Based on the improved DS evidence theory, the position data of the navigation drone and the data from the third-party system are used as the first evidence body, the environmental interference signal strength data as the second evidence body, and the attitude data of the navigation drone as the third evidence body, forming three evidence bodies. A basic probability allocation function on the recognition framework is then assigned to each of the three evidence bodies. .

[0057] In step S121, A indicates that the spatial grid cell belongs to the non-transmission region, and B indicates that the spatial grid cell does not belong to the non-transmission region. This indicates that it is uncertain whether the spatial grid cell belongs to the non-transmission area. The spatial grid cell is related to the position data of the UAV in the first evidence body.

[0058] Understandably, the basic probability allocation function m_i for each piece of evidence is... The calculation rules are shown in Table 1. The three types of data are treated as three independent evidence bodies, and a basic probability allocation function on the recognition framework is assigned to each of the three evidence bodies. ,in, A: "This area is a non-transmission zone" (must meet the interference exceeding the threshold and be within the controlled area). B: "This area is not a non-transmission zone" (interference does not exceed the threshold or is not within the controlled area). "Uncertain" (insufficient or conflicting data) Table 1. Basic probability allocation function m_i for each piece of evidence. Calculation rules

[0059] S122, obtain the historical error rate of each of the three evidence bodies, and calculate the credibility weight of each of the three evidence bodies based on the historical error rate of each of the three evidence bodies.

[0060] Understandably, the credibility weight of the i-th piece of evidence can be calculated based on the historical error rate of each piece of evidence. , the formula is as formula (1) (1) In equation (1), The historical error rate of the i-th piece of evidence can be obtained through statistical analysis of historical data. Indicates the reliability of the i-th piece of evidence. This represents the summation of the reliability of all pieces of evidence.

[0061] For example, the historical error rate of the first body of evidence is... (1% error rate, due to differential positioning and format verification), the historical error rate of the second piece of evidence is... (2% error rate, affected by environmental noise), the historical error rate of the third body of evidence is (3% error rate, static reliability, slightly lower dynamic reliability) If attitude angle deviation > 5°, When it rises to 0.08, the weight decreases accordingly.

[0062] According to equation (1), under normal posture, the credibility weight of the first piece of evidence is: The credibility weight of the second piece of evidence is: The credibility weight of the third piece of evidence is: .

[0063] Step S123: Detect the conflict coefficients of each pair of evidence in the three evidence bodies, and in the case of serious conflict, modify the basic probability allocation function of each evidence body in the three evidence bodies according to the credibility weight of each evidence body in the three evidence bodies, so as to obtain the modified basic probability allocation function of each evidence body in the three evidence bodies.

[0064] Understandably, if the conflict coefficient of two pieces of evidence... ,in, If the two pieces of evidence are found to be seriously conflicting, the conflicting evidence must be corrected before they can be merged.

[0065] Conflict correction rule: The conflict probability is assigned to A and B according to the weight, and the correction formula is as shown in equation (2). (2)

[0066] Step S124: Based on the credibility weight of each of the three evidence bodies, the modified basic probability allocation function of each of the three evidence bodies is weighted and averaged to obtain the basic probability allocation function of each of the three evidence bodies after weighted average. The DS combination rule is then applied to iteratively fuse the basic probability allocation function of each of the three evidence bodies after weighted average to obtain the fusion probability of each spatial grid cell.

[0067] For example, by using the "weighted average + DS combination rule" to fuse the corrected evidence, the final fusion probability function m( ), the formula is as formula (3).

[0068] (3)

[0069] In equation (3), Y is the complement of X (e.g., when X=A, Y=B).

[0070] Step S125: Determine the region division result of the spatial grid cell based on the fusion probability of each spatial grid cell, and generate the boundary parameters of the initial non-transmission region based on the region division result of the spatial grid cell.

[0071] In step S125, the regional division results of the spatial grid cells include spatial grid cells belonging to the non-transmission region, spatial grid cells not belonging to the non-transmission region, and spatial grid cells of uncertain nature belonging to the non-transmission region.

[0072] For example, the determination rule is as follows: If m(A)≥0.8 and m(B)≤0.1: the spatial grid cell is determined to belong to the non-transmission region; If m(B)≥0.8 and m(A)≤0.1: the spatial grid cell is determined not to be a non-transmission cell; If 0.1 < m(A) < 0.8 or 0.1 < m(B) < 0.8: it is determined whether the spatial grid cell belongs to the non-transmission area.

[0073] In some embodiments, such as Figure 5 As shown, step S12 also includes step S126.

[0074] Step S126: If the spatial grid cell region division result is uncertain whether the spatial grid cell belongs to the non-transmission area, the navigation drone increases the collection frequency of environmental perception data and third-party system data, and re-executes steps S121 to S125.

[0075] For example, the sampling frequency of GNSS can be increased to 20 Hz, and the sampling frequency of radio frequency sensors can be increased to 40 million samples per second (MSPS).

[0076] In some embodiments, such as Figure 6 As shown, in step S125, the boundary parameters of the initial non-transmission zone are generated based on the regional division results of the spatial grid cells, including steps S1251 to S1255.

[0077] Step S1251: Mark the spatial grid cells that belong to the non-transmission area as valid non-transmission areas.

[0078] Understandably, according to the judgment rule in step S25, only spatial grid cells with m(A)≥0.8 will be marked as valid non-transmission areas. When generating planar boundaries, height boundaries, and time boundaries in the future, only the grids and ranges marked as valid non-transmission areas will be used, and areas that do not pass through will be eliminated.

[0079] Step S1252: Obtain the plane boundary of the controlled area based on data from a third-party system. Use the union of the effective non-transmission area and the plane boundary of the controlled area as the initial vertex set. Use the Graham scan method to generate convex polygons as the plane boundary.

[0080] For example, the initial boundary is the intersection of the control zone plane boundary of the third-party system (such as the four vertices of the rescue zone) and the radio frequency interference exceeding the threshold area (the union of a 10m × 10m grid). A convex polygon is generated using the Graham scan method (avoiding boundary intersections). The steps are as follows: select the point with the smallest y-coordinate in the vertex set as the starting point P0 (if there are multiple points, select the one with the smallest x-coordinate); sort the other vertices according to their polar angle with P0 (counterclockwise); for vertices with the same polar angle, sort them from closest to farthest from P0; add the vertices to the convex hull sequentially; if the angle formed by the current three points (P1, P2, P3) is ≤180° (cross product ≤0), delete P2, until all vertices have been processed. The final convex polygon has ≥3 and ≤10 vertices (to avoid excessive vertices increasing the computational load on the UAV); the latitude and longitude of each vertex are preserved to 7 decimal places (e.g., 113.2567890°E, 23.1456789°N); and the distance between adjacent vertices is ≥10 meters (to ensure clear boundaries).

[0081] Step S1253: Obtain the height range data of the controlled area based on the data from the third-party system, take the union of the height range data of the effective non-transmission area and the controlled area as the basic height range, and generate the height boundary according to the preset dynamic interference threshold.

[0082] In step S1253, the preset dynamic interference threshold is dynamically set according to the scenario of high layering and effective non-transmission area.

[0083] For example, the base height range is the union of the height range of the third-party system and the height range of radio frequency interference. For instance, the union of 0-80 meters in the emergency rescue zone and 50-120 meters in the high-voltage line range is 0-120 meters. The preset dynamic interference thresholds are shown in the table. Figure 2 As shown.

[0084] Table 2 Examples of preset dynamic interference thresholds

[0085] In Table 2, the preset dynamic interference thresholds vary depending on the altitude level and the scenario. The smallest unit of the altitude range is 1 meter, such as 0-80 meters (not 0-80.5 meters), which facilitates rapid judgment by the drone.

[0086] Step S1254: Determine the earliest value of the effective time and the latest value of the expiration time based on the data from the third-party system. Use the earliest value of the effective time as the effective start time of the time boundary and the latest value of the expiration time as the expiration time of the time boundary.

[0087] For example, the effective time is taken as the earliest value of the effective time of the third-party system. For example, the emergency system takes effect at 14:00 and the transportation system takes effect at 14:10, with 14:00 as the effective start time (Coordinated Universal Time (UTC), accurate to the second). The expiration time is taken as the latest value of the expiration time of the third-party system. If a system does not set an expiration time (such as permanent control), the default expiration time is "current time + 24 hours", which is automatically updated every 24 hours (by re-acquiring third-party data to confirm whether it continues).

[0088] Step S1255: Generate the initial non-transmission zone boundary parameters based on the effective start time of the planar boundary, height boundary, time boundary, and failure time of the time boundary.

[0089] Step S13: Update the boundary parameters of the initial non-transmission area in real time to determine the real-time boundary parameters of the non-transmission area.

[0090] For example, if no event is triggered, the data can be re-fused every 30 minutes to update the boundary parameters. Even if the parameters do not change, a new version number is generated for the drone to confirm the timeliness of the data.

[0091] In some embodiments, S13 includes: in response to the occurrence of any one of at least one preset event, updating the boundary parameters of the initial non-transmission region in real time to determine the real-time boundary parameters of the non-transmission region. The at least one preset event includes: the appearance of a new interference source, a change in the location of an existing interference source, an update of data from a third-party system, and a decrease in the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission region.

[0092] Understandably, in addition to setting the update cycle, to avoid the non-transfer area becoming "static", four types of preset events can be set. Once the conditions are met, the boundary recalculation will be started immediately to ensure that the non-transfer area is synchronized with the actual scene, as shown in Table 3.

[0093] Table 3 Examples of Preset Events

[0094] For example, after any of the above four types of events occurs, the "dynamic adjustment process" is initiated, with the entire process taking ≤1 second (ensuring real-time performance). The specific steps are as follows: Pause the current boundary transmission: The edge computing node sends a "pause broadcast" command (command code 0x0A) to the ZigBee Pro module to stop transmitting the old boundary parameters from the drone; Record a "snapshot" of the current boundary parameters (including zoneId, version, etc.) for subsequent comparison of the differences before and after the adjustment.

[0095] In some embodiments, such as Figure 7 As shown, the boundary parameters of the initial non-transmission area are updated in real time, including steps S131 to S132.

[0096] Step S131: Increase the collection frequency of environmental perception data and third-party system data, and perform data fusion based on the latest environmental perception data and third-party system data obtained after increasing the collection frequency to generate updated boundary parameters of the non-transmission area.

[0097] For example, the acquisition frequency of multi-source data can be temporarily increased, such as increasing the acquisition frequency of GNSS to 20Hz, the RF sensor to 40MSPS, and shortening the data request interval of the third-party system to 5 seconds. Based on the environmental perception data of the most recent second obtained after increasing the acquisition frequency and the data from the third-party system, data fusion is performed to generate updated boundary parameters of the non-transmission area to ensure data timeliness.

[0098] Step S132: Compare the updated boundary parameters of the non-transfer region with the initial boundary parameters of the non-transfer region. If the comparison result meets the preset update conditions, the updated boundary parameters of the non-transfer region are used as the real-time boundary parameters of the non-transfer region; otherwise, the initial boundary parameters of the non-transfer region are used as the real-time boundary parameters of the non-transfer region.

[0099] In step S132, the preset update conditions include: the overlap rate of the planar boundary is less than 80%, the change in height range exceeds 20 meters, and the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission area decreases by more than 0.1.

[0100] For example, key differences between the old and new boundary parameters can be compared (planar boundary overlap rate < 80%, height range change > 20 meters, confidence change > 0.1). If the difference is < 5% (minor adjustment), the adjustment is cancelled and the old boundary is restored.

[0101] In some embodiments, step S1 further includes step S14.

[0102] Step S14: If a fault is detected, report the fault and use an emergency update method. For example... Figure 8 As shown, step S14 includes steps S141 to S144.

[0103] Step S141: If the position data of the pilot drone is not obtained within the first preset time, the position data of the pilot drone is calculated by fusion positioning of IMU and barometric altimeter, and the planar boundary of the non-transmission area is expanded.

[0104] For example, the first preset time period is 10 seconds. If GNSS data is lost and the duration is greater than 10 seconds, the method of fusion positioning of IMU and barometric altimeter is enabled. The positioning error is ≤1 m / s (the error is ≤10 m within the short term of 10 seconds). The plane boundary of the non-transmission area is expanded by 10 meters (as a safety margin) to avoid the UAV misjudging the area due to positioning error.

[0105] Step S142: If no environmental interference signal strength data is collected within the second preset time period, the interference data and historical environmental interference signal strength data from the third-party system are used to replace the real-time environmental interference signal strength data, the dynamic interference threshold is lowered, and the fault is reported.

[0106] In step S142, the dynamic interference threshold is used to generate the height boundary of the initial non-transmission area.

[0107] For example, the second preset time period is 5 seconds. If the radio frequency sensor fails and the time period without data output is greater than 5 seconds, interference data from a third-party system and historical radio frequency data are used as substitutes. The dynamic interference threshold is lowered by 5dBm (more stringent) to ensure that no interference source is missed. The sensor failure incident is reported to the relevant base station, triggering the base station to dispatch a backup drone (e.g., carrying a backup radio frequency sensor) to supplement the sensing.

[0108] Step S143: If no data is collected from the third-party system within the third preset time period, extend the failure time of the time boundary and attempt to collect data from the third-party system again according to the preset collection frequency.

[0109] For example, the third preset time period is 5 minutes. If the third-party system is disconnected for more than 5 minutes, the last received third-party data will be used, but the effective time will be extended to "current time + 1 hour". The connection to the third-party system will be retried every hour.

[0110] Step S144: If no data from a third-party system is collected within the fourth preset time period, the height boundary of the non-transmission area is expanded and the dynamic interference threshold is lowered.

[0111] For example, the fourth preset time period is 2 hours. If the third-party system is disconnected for more than 2 hours, the height range of the non-transmission area is expanded to 0-150 meters, and the dynamic interference threshold is lowered by 10dBm to ensure coverage of potential unreported interference sources.

[0112] Understandably, after generating the non-transmission zone boundary parameters, the pilot drone encapsulates them according to a standardized format. This standardized format includes: a list of plane vertex coordinates (latitude and longitude are rounded to 7 decimal places), an altitude range (minimum unit is 1 meter), the effective start time and expiration time of the time boundary (UTC time, accurate to the second), a version number (incrementing by 0.1 with each update), a timestamp (generation time), and a verification field (SHA256 hash value of all fields).

[0113] The lead drone sends the packaged data to the slave drones. After receiving the data, the slave drone first calculates the SHA256 hash value of all fields and compares it with the verification field. If they do not match, it requests a retransmission from the lead drone (up to 2 requests). If the received version number is not greater than the locally stored version number and the timestamp has not expired, the data is discarded to avoid duplicate processing.

[0114] For example, the lead drone can also send non-transmission zone boundary parameters to slave drones via a ZigBee Pro Mesh network. For instance, the lead drone broadcasts the current non-transmission zone boundary parameters every 10 seconds, and slave drones reply with an acknowledgment message containing their own identifier within 100 milliseconds of receiving the broadcast. If the lead drone does not receive an acknowledgment message from a slave drone, it unicasts and retransmits the parameters to that slave drone, up to three times, to ensure that the boundary parameters are reliably delivered to all slave drones.

[0115] Understandably, the purpose of steps S2 to S5 is to solve the problem of "misjudgment caused by positioning error" when a single UAV independently determines a non-transmission zone. By using cluster collaboration (mesh network data synchronization + cross-verification of neighboring nodes), the judgment error of a single UAV is reduced. At the same time, it ensures that multiple UAVs have consistent judgments on "whether they are in a non-transmission zone", avoiding some UAVs from misjudging out of the zone and causing illegal transmission (causing interference) or misjudging into the zone and causing key data to be missed. This provides an accurate "regional attribution basis" for subsequent hierarchical transmission control and ensures the consistency and reliability of cluster communication.

[0116] Understandably, the lead drone and the slave drones form a mesh network using the ZigBee Pro distributed protocol (frequency band 2.4GHz, transmission rate 250kbps, communication distance 500 meters), synchronizing perception data every 100ms to avoid perception errors of a single drone. The network topology configuration is shown in Table 4.

[0117] Table 4 Hardware and Network Topology Configuration

[0118] To ensure the time consistency of multi-machine sensing data (error ≤ 10ms), a dedicated synchronization frame and transmission mechanism are designed. The structure of the synchronization frame is shown in Table 5.

[0119] Table 5. Example of Synchronization Frame Structure

[0120] For example, to ensure synchronization accuracy, a timing control mechanism can be introduced, triggering a full network data synchronization every 100ms (the lead drone sends a "synchronization pulse frame" at 0ms, 100ms, 200ms..., the pulse frame contains only a frame header and timestamp, is 12 bytes long, and takes ≤50μs to transmit). After receiving the synchronization pulse, the drone sends its own sensing data within 5ms (sent in ascending order by ID to avoid channel conflicts: 0x0002 is sent between 5-10ms, 0x0003 between 10-15ms, etc.). The drone compares its local clock with the timestamp of the synchronization pulse; if the deviation is >1ms, it is calibrated through "phase fine-tuning" (adjusting by 0.1ms each time to avoid clock jumps) to ensure that the full network clock synchronization error is ≤1ms.

[0121] For example, in terms of reliability assurance, the lead drone maintains a "node status table" to record the synchronization frame reception status of each slave drone (if it fails to receive a synchronization frame for 3 consecutive times, it is marked as "offline"); if the slave drone does not receive a synchronization pulse, it actively sends a "clock request frame" (once every 50ms until a response is received).

[0122] Retransmission strategy: Minor packet loss (single frame loss): The lost data is retransmitted from the UAV in the next synchronization cycle (100ms later) (the frame header is marked with 0x5B5B to indicate retransmission). Severe packet loss (two consecutive frames lost): "Emergency retransmission" is triggered, and the UAV suspends non-critical tasks and prioritizes sending retransmission frames (occupying a dedicated emergency time slot, with the highest priority).

[0123] Interference tolerance: "Frequency hopping" anti-interference is adopted: the channel is switched every 10 synchronization cycles (based on pseudo-random sequence, the hopping pattern is negotiated in advance) to avoid batch packet loss caused by continuous interference.

[0124] To adapt to dynamic changes in drone swarms (such as some drones joining / leaving), the following management mechanism is designed: Node joining process: After a new drone (e.g., drone 6) powers on, it sends a "network joining request frame" (broadcast). Upon receiving the frame, the lead drone assigns a unique ID (0x0006) and sends the current network parameters (channel, encryption key, synchronization period). After the new node joins, it automatically sends "neighbor discovery frames" to the three nearest nodes, establishes a routing table (recording the next-hop node ID and RSSI), and completes the network joining process (time ≤ 1 second).

[0125] Load balancing strategy: When the communication load of a slave drone (frames sent per second > 50) is too high, the lead drone distributes some of its data forwarding tasks to nodes with lower loads (e.g., slave drone 2 with a load of 10 frames / second), reducing the pressure on a single node through "data offloading". The shortest path across the entire network is calculated every 5 seconds (based on Dijkstra's algorithm) to avoid data taking long detours (reducing transmission latency by 10-20ms).

[0126] Node exit handling: When a drone voluntarily exits (e.g., upon mission completion), it sends a "de-network notification frame," and surrounding nodes update their routing tables (deleting entries related to that node). Drone abnormally offline (e.g., power outage): After other nodes detect five consecutive communication failures, they automatically trigger route reconstruction (a new route is established within 50ms).

[0127] Understandably, in step S2, the local determination result is obtained by the UAV determining whether it is located in the dynamic non-transmission zone based on the real-time boundary parameters of the non-transmission zone and the state perception data after acquiring its own state perception data.

[0128] For example, the first method for determining the drone specifically includes: 1. Locally determine input data Dynamic non-transfer zone parameters: "3D boundary parameters" (planar convex polygon vertices, height layering thresholds, effective time, etc.) synchronized from the pilot drone.

[0129] Self-status data: GNSS position (latitude, longitude, altitude), IMU attitude angle (to ensure no violent shaking during positioning), timestamp (to verify whether it is within the effective time).

[0130] 2. Local decision-making algorithm (spatial geometry + time verification)

[0131] Planar position determination: The "ray method" is used to determine whether the latitude and longitude lie within a convex polygon in the plane. First, launch a ray from the drone's position (P) in any direction (e.g., due east). Count the number of intersections between the ray and the polygon's edges. If the number of intersections is odd, then P is inside the polygon; if it is even, then P is outside the polygon (points on the boundary are considered "inside"). Calculate the distance from P to the nearest edge (if it is <0.5 meters, it is considered "near the boundary," and further verification is needed).

[0132] Height determination: Compare its own height with the height of the dynamic non-transmission area for layering: If the altitude is within the range of [min, max], the altitude condition is met. If the altitude is <min or >max, the altitude condition is not met (±0.5 meters error is allowed due to slight drift of the barometric altimeter).

[0133] Time determination: Check if the current timestamp is within the [start, end] interval (allow ±1 second error due to network synchronization delay).

[0134] Comprehensive Judgment Rules: If the conditions of "plane within the zone + height within the range + time within the interval" are met, it is initially determined that the area is "located in the non-transmission zone".

[0135] If any condition is not met, it is initially determined that the location is "not located in the non-transmission area".

[0136] Assuming N is 3, when the first drone continuously verifies three times (each time with a 50ms interval, for a total of 150ms) that it is "located in a non-transmission area", collaborative verification is triggered: Immediately send a "pending confirmation signal" (frame type 0x05, containing its own ID, timestamps of 3 judgments, and location data) to the lead drone.

[0137] If the system determines three consecutive times that the drone is "not located in a non-transmission area," it is only recorded locally and does not need to be reported (to reduce communication volume). After receiving the "pending confirmation signal," the navigation drone does not directly accept the data from a single drone, but instead performs cross-verification by combining the "proximity data" from multiple drones.

[0138] For example, step S3 includes: the lead drone sends a "data request frame" (0x06) to the three neighboring nodes of the "slave drone to be confirmed" (obtained from the neighbor list of the Mesh network, the three closest drones), requesting: the relative position of the slave drone (e.g., slave drone 3 reports "slave drone 2 is 25 meters due north of me, at the same altitude"), its own local determination result of the dynamic non-transmission area (used to exclude overall positioning deviation), and the signal direction of the slave drone detected by the radio frequency sensor (to assist in positioning). The neighboring nodes are required to reply with data within 10ms (if no reply is received within the timeout period, the node's data is discarded).

[0139] In some embodiments, such as Figure 9 As shown, step S4 includes steps S41 to S413.

[0140] Step S41: Calculate the position consistency score and the height consistency score based on the local determination result of the first slave drone's position and the relative position observations of each slave drone among at least one adjacent slave drone to the first slave drone.

[0141] Understandably, the local determination result of the first drone's location includes the drone's location information and altitude information.

[0142] Step S42: Based on the local determination results of the positions of each of the at least one adjacent drone, the proportion of the number of drones whose determination results are located in the non-transmission area is statistically analyzed, and the determination consistency score is calculated based on the proportion.

[0143] Step S43: The position consistency score, the height consistency score, and the judgment consistency score are weighted and summed to obtain the collaborative consistency score.

[0144] For example, the collaborative consistency score is used to comprehensively evaluate the consistency of multiple data sources, as illustrated in steps S41 to S43, as follows: Position consistency (weighted by 60 points): Calculate the distance deviation d (d=√[(x1-x2)²+(y1-y2)²]) between the position (P2) deduced from the relative position reported by the UAV (P1) and the position (P2) reported by neighboring nodes.

[0145] Position consistency score = max(0, 1-d / 5) (full score when d≤5 meters, 0 points when d>5 meters, because within 5 meters is acceptable as measurement error).

[0146] High consistency (weighted by 20 points): Compare the deviation Δh = |h1-h2| between the drone's altitude (h1) and the altitude (h2) reported by neighboring nodes.

[0147] High consistency score = max(0, 1-Δh / 3) (full score when Δh ≤ 3 meters, 0 points when Δh > 3 meters).

[0148] Consistency determination (weighted by 20 points): Calculate the percentage k of the number of neighboring nodes that are locally determined to be "located in the non-transmission area" (if 2 out of 3 nodes are determined to be consistent, then k = 66.7%).

[0149] Consistency score = k.

[0150] Collaborative consistency score S = 60 × position score + 20 × high consistency score + 20 × decision consistency score.

[0151] In some embodiments, the preset threshold includes a first preset threshold and a second preset threshold, and the collaborative verification result instruction includes a first collaborative verification result instruction, a second collaborative verification result instruction, and a third collaborative verification result instruction. S5 includes: if the collaborative consistency score is greater than or equal to the first preset threshold, generating a first collaborative verification result instruction. The first collaborative verification result instruction indicates that the first slave drone is located in a non-transmission area. If the collaborative consistency score is greater than or equal to the second preset threshold but less than the first preset threshold, generating a second collaborative verification result instruction instructing the first slave drone to increase its positioning sampling frequency and re-report the judgment result after a preset time. If the collaborative consistency score is less than the second preset threshold, generating a third collaborative verification result instruction. The third collaborative verification result instruction instructs the first slave drone to enable RTK differential positioning, recalculate its position, and re-report the judgment result.

[0152] For example, taking a first preset threshold of 90 and a second preset threshold of 70 as an example. If S≥90 (high consistency) → the lead drone sends the first collaborative verification result instruction (e.g., 0x07) to the slave drone and records the slave drone's status inside and outside the area.

[0153] If 70 points ≤ S < 90 points (medium consistency) → issue a second collaborative verification result instruction (e.g., 0x08), requiring the UAV to increase the GNSS sampling rate (20Hz) and re-report the judgment result after 500ms.

[0154] If S < 70 (low consistency) → trigger the “GNSS relocation” process: the lead drone sends a third collaborative verification result instruction (e.g., 0x09) to the slave drone, requiring it to enable RTK differential positioning (error ≤ 0.1 meters), receive differential data from the slave drone (obtained from the base station via the 5G NR link), recalculate the position and report it.

[0155] In some embodiments, step S5 further includes: in response to receiving data from the first drone that has re-reported the judgment result based on the second collaborative verification result instruction and the third collaborative verification result instruction, recalculating the collaborative consistency score; if the recalculated collaborative consistency score is still less than the second preset threshold, generating and reporting device abnormal information.

[0156] For example, the pilot drone recalculates the consistency S based on the new location. If S is still less than 70 points, it is judged as "device abnormal" and reported to the base station (which may be due to sensor failure).

[0157] In some embodiments, the communication method of the drone further includes step S6.

[0158] Step S6: Obtain the current flight scenario type, and determine the number of each slave drone in at least one adjacent slave drone and a preset threshold based on the current flight scenario type. Scenario types include densely populated urban areas, open areas, or emergency rescue areas.

[0159] Understandably, to avoid misjudgments in complex scenarios using a fixed threshold (e.g., S≥90 points), a "scenario-adaptive threshold" mechanism is designed: In densely populated urban areas (with many buildings obstructing the view): the positioning error is relatively large, and the consistency threshold is lowered to S≥80 (allowing for greater deviation). The number of neighboring nodes is increased (from 3 to 5) to offset the impact of obstruction with more data.

[0160] In open areas (such as plains and lakes): positioning accuracy is high, and the consistency threshold is increased to S≥95 points (strict judgment). The number of neighboring nodes is reduced (from 3 to 2) to reduce communication load.

[0161] Emergency rescue zone (high interference): Radio frequency signals are easily interfered with. The weight of "location consistency" is reduced from 60 points to 50 points, while the weight of "judgment consistency" is increased from 20 points to 30 points (more dependent on multi-machine consensus).

[0162] Threshold adjustment trigger: The lead drone automatically adjusts the threshold every 30 seconds based on the current scene type (obtained from data from a third-party system), and the adjustment result is synchronized to all slave drones.

[0163] In some embodiments, the hierarchical dynamic transmission mechanism includes: for urgent data, regardless of whether the first slave drone is located in a non-transmission zone, interrupting the currently ongoing non-urgent transmission task and reporting the urgent data in real time via an ultra-wideband (UWB) link using frequency hopping technology. For important data, if the first slave drone is located in a non-transmission zone, the important data is cached in local storage; if the first slave drone is not located in a non-transmission zone, the cached important data is reported via a 5G link. For ordinary data, it is compressed and cached using a preset compression algorithm, and uploaded via a narrowband Internet of Things (NB-IoT) link during a preset low-power communication period.

[0164] Understandably, the purpose of setting up a hierarchical dynamic transmission mechanism is to resolve the transmission conflict of data with different levels of urgency on the basis of "suppressing uplink interference" in non-transmission areas, avoid unnecessary transmissions that aggravate interference, ensure that critical data is not delayed or lost, adapt to dynamic channel environments, and ultimately achieve a communication balance of controllable interference, available data, and optimal energy consumption.

[0165] Understandably, a three-tiered data classification system is constructed based on the degree of data's impact on mission safety and system control, providing a basis for differentiated transmission. By analyzing the core requirements of typical UAV missions, the core characteristics and quantitative indicators of the three-tiered data are defined, as shown in Table 6.

[0166] Table 6 Examples of Three Types of Data

[0167] For example, the implementation details of the hierarchical transmission mechanism are as follows: Emergency data (real-time transmission outside the transmission area): The system employs an ultra-wideband (UWB) low-power link (3.5GHz frequency band, 1Mbps transmission rate, ≤10mW power consumption). The frame structure is designed as follows: preamble (32 bytes) + data type identifier (1 byte, 0x01 indicates urgency) + data payload (256 bytes) + CRC checksum (4 bytes). Upon detecting urgency data, non-urgency tasks are immediately interrupted and prioritized for transmission. Frequency hopping communication (switching one frequency point per frame, for a total of 16 frequency points) is used to avoid interference. The transmission success rate is required to be ≥99.9%.

[0168] Important data (buffered outside the transmission area + batch transmission after departure): Cache medium: UAV local Flash memory (capacity 128MB, read / write speed 50MB / s), adopting a "circular overwrite" strategy (new data overwrites the oldest non-urgent data).

[0169] Transmission after departure: After the drone leaves the non-transmission area (determined to be outside the area 3 times consecutively), it uploads data in batches via the 5G link (licensed frequency band 2.6GHz, transmission rate 100Mbps), and adopts the HARQ retransmission mechanism (maximum 3 retransmissions) to ensure that the packet loss rate is ≤0.1%.

[0170] Ordinary data (non-transmission zone compressed buffer + timed reporting): LZ4 compression is used (compression ratio 2:1, compression speed 400MB / s) to reduce buffer usage; from 00:00 to 02:00 every day (channel idle period), it is uploaded through NB-IoT low power link (power consumption ≤5mW) to reduce interference to other services.

[0171] In addition, the ground base station, as the "overall command center," proactively coordinates resources, optimizes processes, and responds to emergencies. Specifically, it performs three key tasks to ensure the efficient and stable operation of the drone swarm. These functions include: (1) After receiving the data reported by the UAV, the ground base station automatically assigns it to the corresponding processing module through the "data classification engine": emergency data is sent to the emergency dispatch center (response time ≤ 1 second), important data is stored in the database (using MySQL cluster, storage capacity 10TB), and ordinary data is archived to cloud storage (Alibaba Cloud OSS).

[0172] (2) In response to the coexistence of multiple links (5G NR, UWB, ZigBee, NB-IoT) in UAV swarms, the base station constructs a "spectrum resource pool" to allocate channels and time slots in real time to avoid co-channel interference. For example, when a UAV needs to transmit data, the base station will specify which link (UWB for emergency data, 5G for important data, and NB-IoT for ordinary data) and which channel (to avoid congestion caused by multiple UAVs using the same channel at the same time), such as "UAV 2 transmits important data using 5G sub-channel 10, and UAV 3 transmits emergency data using UWB frequency 3.5GHz".

[0173] Specifically: 1. Construction of spectrum resource pool Resource classification: Sub-pools are divided according to link type, including: 5G NR sub-pool: 2.6GHz licensed frequency band (100MHz bandwidth, divided into 50 2MHz sub-channels); UWB sub-pool: 3.5GHz / 4.0GHz unlicensed frequency bands (200MHz bandwidth each, 10MHz per frequency point, for a total of 40 frequency points); ZigBee sub-pool: 2.4GHz band (16 channels, channels 11-26); NB-IoT sub-pool: 800MHz band (3 narrowband channels, 180kHz each).

[0174] Resource status monitoring: The base station updates the "occupancy rate" (e.g., 5G sub-channel occupancy rate = number of drones in use / maximum number of supported drones) and "interference intensity" (e.g., if the interference of a certain UWB frequency point is >-60dBm, it is marked as "unavailable") in real time through the "spectrum scanning module" (sampling rate 10MHz, scan once per second).

[0175] 2. Dynamic allocation strategy (priority + load balancing)

[0176] Prioritization: Allocate resources according to data level (urgent data > important data > ordinary data), example: Emergency Data (UWB): Priority will be given to allocating frequency points with interference intensity < -70dBm. If resources are insufficient, frequency points of low-priority links can be preempted (e.g., temporarily occupying ZigBee channels for a duration of ≤ 1 second). Important data (5G NR): Allocate sub-channels with an occupancy rate of <30% to ensure a transmission rate of ≥50Mbps; Regular data (NB-IoT): Allocated only when the channel idle rate is >90% (such as during the early morning hours).

[0177] Load balancing: When the resource load of a sub-pool exceeds 70% (e.g., 80% of drones occupy a 5G sub-channel), the base station triggers "resource migration": Migrate some important data from 5G NR to an idle ZigBee Mesh network (when the speed requirement is <1Mbps); Delay the transmission of normal data (postpone it until the load is less than 50%) and notify the drone to extend the buffer time.

[0178] 3. Distribution and feedback of allocation results

[0179] Every 100ms, the base station generates a "spectrum allocation table" (JSON format), which includes the "link type + channel / frequency point + time slot" of each drone (e.g., 5G sub-channel 10 is available from 10:00:00.000 to 10:00:00.100), and broadcasts it through the 5G control channel.

[0180] The drone will reply with an "acknowledgment frame" within 10ms after receiving the message. If the allocated resources are unavailable (e.g., strong interference is detected on the local channel), the base station will reallocate backup resources within 50ms.

[0181] (3) The base station will first assess the capabilities of each drone (such as battery power and sensor accuracy) and then assign tasks—letting drones with sufficient batteries and accurate sensors chase the interference source, and letting drones with low power and average capabilities transmit historical logs, so as to avoid “weak drones doing heavy work and strong drones being idle.”

[0182] (4) When a single cluster (such as cluster A) has insufficient resources (e.g., the number of drones is less than the mission requirements, or spectrum resources are exhausted), the base station initiates "cross-cluster collaboration" to call upon the redundant resources of a neighboring cluster (such as cluster B): 1. Inter-cluster communication interface Data interaction is achieved using the "5G NR-Xn interface" (standard interface between base stations), with a transmission rate of ≥1Gbps and a latency of <20ms.

[0183] Interactive data includes: resource gaps in cluster A (e.g., "2 interference tracking drones are needed") and a list of redundant resources in cluster B (e.g., "3 drones with scores ≥ 9 are available").

[0184] 2. Collaborative Scheduling Process

[0185] A cluster base station sends a "resource request" (including gap type, quantity, and priority) to the core network; The core network queries the status of neighboring clusters and selects cluster B, which has the highest resource redundancy, as the support. The B cluster base station dispatched two backup drones to the A cluster area with "cross-cluster flight permission" (authorized by the air traffic control system); Once the support drone arrives, it connects to the Mesh network of Cluster A, and tasks are assigned by the base station of Cluster A.

[0186] 3. Collaboration Termination Mechanism

[0187] When the resource gap in cluster A is resolved (e.g., task completion, new drone joining), the base station sends a "return command" to the supporting drone and notifies the base station of cluster B to update the resource list.

[0188] The embodiments disclosed herein significantly improve the communication reliability and security of drone swarms in dynamic electromagnetic environments by dynamically generating non-transmission zone boundaries using a navigation drone, coordinating perception and cross-verification among multiple drones, and implementing hierarchical transmission control based on data urgency levels. Specifically, multi-source data fusion and DS evidence theory ensure the real-time accuracy of non-transmission zone boundaries, avoiding excessive or missed suppression caused by fixed restricted areas; the collaborative consistency scoring model reduces the area determination error from ±3 meters to ±0.3 meters, effectively preventing misjudgments and unauthorized transmissions caused by single-drone positioning deviations; the hierarchical transmission mechanism employs UWB+ frequency hopping for real-time reporting of urgent data, 5G batch reporting for important data after caching, and NB-IoT uploading during idle hours after compression of ordinary data, which not only suppresses unnecessary uplink interference but also ensures low-latency, high-reliability transmission of critical data, while reducing overall power consumption and improving mission endurance.

[0189] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0190] This disclosure also provides a communication device 100 for a drone, applied to a navigation drone. Details of the technical solution and beneficial effects of the drone communication device 100 can be found in the specific descriptions of steps S1 to S5, and will not be repeated here. Figure 10 As shown, the communication device 100 of the UAV includes a real-time boundary parameter determination module 101 for the non-transmission area, a data request frame sending module 102, a data receiving module 103, a collaborative consistency score calculation module 104, and a collaborative verification result instruction generation module 105.

[0191] The real-time boundary parameter determination module 101 for the non-transmission area is used to determine the real-time boundary parameters of the non-transmission area and send the real-time boundary parameters of the non-transmission area to each of the slave drones in at least one slave drone.

[0192] The data request frame sending module 102 is configured to, in response to receiving a pending confirmation signal from a first slave drone, send a data request frame to each of the at least one group of neighboring slave drones, so that each of the at least one group of neighboring slave drones can perceive the position of the first slave drone based on the data request frame and report neighbor perception data. The first slave drone is one of the at least one group of slave drones. The pending confirmation signal is issued by the first slave drone after N consecutive local determinations indicating that it is located within a non-transmission zone. The local determination result is obtained by the first slave drone after acquiring its own state perception data, based on the real-time boundary parameters of the non-transmission zone and the state perception data, determining whether it is located within a dynamic non-transmission zone. The state perception data includes at least position data and time data. The pending confirmation signal includes the local determination result and the state perception data. N is a positive integer. Each of the at least one group of neighboring slave drones is a slave drone that is positionally adjacent to the first slave drone. The neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring drones to the first drone, as well as the local determination results of the position of each of the at least one neighboring drones.

[0193] The data receiving module 103 is used to receive neighbor perception data from each of the slave drones in at least one neighboring slave drone.

[0194] The collaborative consistency score calculation module 104 is used to calculate the collaborative consistency score based on the signal to be confirmed and the neighbor perception data from each of the slave drones in at least one neighboring slave drone.

[0195] The collaborative verification result instruction generation module 105 is used to compare the collaborative consistency score with a preset threshold, generate a collaborative verification result instruction, and send the collaborative verification result instruction to the first slave drone, so that the first slave drone can determine the final regional affiliation status according to the collaborative verification result instruction, and transmit data according to the final regional affiliation status in accordance with the hierarchical dynamic transmission mechanism.

[0196] This disclosure also provides a communication method for a drone, applied to a first slave drone, which is one of at least one slave drone. For example... Figure 11 As shown, the method includes steps A1 to A5. Details of the technical solutions and beneficial effects of steps A1 to A5 can be found in the specific descriptions of steps S1 to S5, and will not be repeated here.

[0197] Step A1: Receive real-time boundary parameters from the non-transmission zone of the pilot drone.

[0198] Step A2: Obtain the first state perception data from the UAV itself. The state perception data includes at least location data and time data.

[0199] Step A3: Determine whether the entity is located in the dynamic non-transmission zone based on the real-time boundary parameters and state awareness data of the non-transmission zone, and obtain the local determination result.

[0200] Step A4: If the number of times the local determination result indicates that the first slave drone is located in the dynamic non-transmission zone is greater than N, a confirmation signal is sent to the lead drone so that the lead drone sends a data request frame to each of the at least one adjacent slave drone.

[0201] In step A4, after each of the at least one neighboring drones receives a data request frame, it senses the position of the first drone based on the data request frame, reports neighbor sensing data, calculates a cooperative consistency score based on the signal to be confirmed and the neighbor sensing data from each of the at least one neighboring drone, compares the cooperative consistency score with a preset threshold, generates a cooperative verification result instruction, and sends the cooperative verification result instruction to the first drone. N is a positive integer. Each of the at least one neighboring drones is a drone that is positionally adjacent to the first drone. The neighbor sensing data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local determination results of the position of each of the at least one neighboring drone.

[0202] Step A5: Receive the collaborative verification result instruction from the lead drone, determine the final regional affiliation status based on the collaborative verification result instruction, and transmit data according to the hierarchical dynamic transmission mechanism based on the final regional affiliation status.

[0203] This disclosure also provides a communication device 120 for a drone, applied to a first slave drone, which is one of at least one slave drone. Details of the technical solution and beneficial effects of the drone communication device 120 can be found in the specific descriptions of steps S1 to S5, and will not be repeated here. Figure 12 As shown, the communication device 120 of the UAV includes a boundary parameter receiving module 121, a sensing module 122, a local determination module 123, a confirmation signal sending module 124, and a collaborative verification result instruction receiving module 125.

[0204] The boundary parameter receiving module 121 is used to receive real-time boundary parameters from the non-transmission area of ​​the pilot UAV.

[0205] The perception module 122 is used to acquire first state perception data from the UAV itself, and the state perception data includes at least position data and time data.

[0206] The local determination module 123 is used to determine whether it is located in the dynamic non-transmission zone based on the real-time boundary parameters and state awareness data of the non-transmission zone, and obtain the local determination result.

[0207] The pending confirmation signal sending module 124 is used to send a pending confirmation signal to the lead drone when the number of times the local determination result indicates that the first slave drone is located in the dynamic non-transmission area is greater than N. This causes the lead drone to send data request frames to each of the at least one neighboring slave drone, enabling each of the at least one neighboring slave drone to perceive the position of the first slave drone based on the data request frames, report neighbor perception data, calculate a cooperative consistency score based on the pending confirmation signal and the neighbor perception data from each of the at least one neighboring slave drone, compare the cooperative consistency score with a preset threshold, generate a cooperative verification result instruction, and issue the cooperative verification result instruction to the first slave drone. N is a positive integer. Each of the at least one neighboring slave drones is a slave drone that is positionally adjacent to the first slave drone. The neighbor perception data includes the relative position observations and signal directions of each of the at least one neighboring slave drones relative to the first slave drone, as well as the local determination results of the position of each of the at least one neighboring slave drones.

[0208] The collaborative verification result instruction receiving module 125 is used to receive collaborative verification result instructions from the pilot UAV, determine the final regional affiliation status based on the collaborative verification result instructions, and transmit data according to the hierarchical dynamic transmission mechanism based on the final regional affiliation status.

[0209] In addition, this disclosure also provides an electronic device and a computer-readable storage medium, all of which can be used to implement steps S1 to S5 or steps A1 to A5 in any of the communication methods of UAVs provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding records in the method section and will not be repeated here.

[0210] Figure 13 This is a block diagram of an electronic device provided in an embodiment of the present disclosure.

[0211] Reference Figure 13 This disclosure provides an electronic device comprising: at least one processor 1301; at least one memory 1302; and one or more I / O interfaces 703 connected between the processor 1301 and the memory 1302; wherein the memory 1302 stores one or more computer programs executable by the at least one processor 1301, the one or more computer programs being executed by the at least one processor 1301 to enable the at least one processor 1301 to execute the aforementioned communication method of the unmanned aerial vehicle.

[0212] This disclosure also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the aforementioned communication method for the unmanned aerial vehicle (UAV). The computer-readable storage medium may be volatile or non-volatile.

[0213] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the above-described communication method of the UAV.

[0214] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0215] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0216] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0217] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0218] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0219] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0220] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0221] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0222] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0223] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A communication method for an unmanned aerial vehicle (UAV), characterized in that, Applied to piloting drones, the methods include: S1, determine the real-time boundary parameters of the non-transmission zone, and send the real-time boundary parameters of the non-transmission zone to each of the slave drones in at least one slave drone; S2, in response to receiving a pending confirmation signal from a first slave drone, a data request frame is sent to each of at least one neighboring slave drones, so that each of the at least one neighboring slave drones can perceive the position of the first slave drone according to the data request frame and report neighbor perception data; the first slave drone is one of the at least one slave drones; the pending confirmation signal is issued by the first slave drone after N consecutive local determinations that the first slave drone is located in a non-transmission area; the local determination result is that the first slave drone, after acquiring its own state perception data, determines the position of the first slave drone based on the non-transmission area... The system determines whether a device is located within a dynamic non-transmission zone based on real-time boundary parameters and state-aware data. The state-aware data includes at least location data and time data. The signal to be confirmed includes the local determination result and the state-aware data. N is a positive integer. Each of the at least one neighboring drones is a drone that is positionally adjacent to the first drone. The neighbor-aware data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local determination results of the position of each of the at least one neighboring drones. S3, receive neighbor perception data from each of the at least one neighboring drone; S4, calculate the cooperative consistency score based on the signal to be confirmed and the neighbor perception data from each of the at least one neighboring drone; S5, compare the collaborative consistency score with a preset threshold, generate a collaborative verification result instruction, and send the collaborative verification result instruction to the first slave drone, so that the first slave drone determines the final regional affiliation status according to the collaborative verification result instruction, and performs data transmission according to the hierarchical dynamic transmission mechanism based on the final regional affiliation status.

2. The communication method for a UAV according to claim 1, characterized in that, S1 includes: S11, during flight, environmental perception data and data from third-party systems are collected; the environmental perception data includes the position data of the navigation drone, environmental interference signal strength data, and attitude data of the navigation drone; the data from third-party systems includes emergency management data, traffic control data, and power grid electromagnetic interference data; S12, the environmental perception data and the data from the third-party system are fused to generate initial non-transmission zone boundary parameters; wherein, the boundary parameters include the effective start time of the planar boundary, the height boundary, the effective start time of the time boundary, and the effective failure time of the time boundary; S13, update the boundary parameters of the initial non-transmission area in real time, and determine the real-time boundary parameters of the non-transmission area.

3. The communication method for a UAV according to claim 2, characterized in that, In S11, the method for the navigation drone to collect environmental perception data includes: The S111 uses its built-in Global Navigation Satellite System (GNSS) module to obtain the location data of the pilot drone; S112 uses a built-in radio frequency sensor to collect data on the intensity of environmental interference signals; these interference signals include: emergency equipment signals and signals from nearby base stations. The S113 uses its built-in inertial measurement unit (IMU) module to acquire the attitude data of the pilot drone.

4. The communication method for a UAV according to claim 2, characterized in that, S12 includes: S121, based on the improved DS evidence theory, the position data of the navigation drone and the data of the third-party system are used as the first evidence body, the environmental interference signal strength data are used as the second evidence body, and the attitude data of the navigation drone are used as the third evidence body, forming three evidence bodies; a basic probability allocation function on the recognition framework is assigned to each of the three evidence bodies; where A represents that the spatial grid cell belongs to the non-transmission area, B represents that the spatial grid cell does not belong to the non-transmission area, and represents that it is uncertain whether the spatial grid cell belongs to the non-transmission area; the spatial grid cell is related to the position data of the drone in the first evidence body; S122, obtain the historical error rate of each of the three evidence bodies respectively, and calculate the credibility weight of each of the three evidence bodies according to the historical error rate of each of the three evidence bodies respectively. S123, detect the conflict coefficients of each pair of evidence in the three evidence bodies, and in the case of serious conflict, modify the basic probability allocation function of each evidence body in the three evidence bodies according to the credibility weight of each evidence body in the three evidence bodies, and obtain the modified basic probability allocation function of each evidence body in the three evidence bodies. S124, according to the credibility weight of each of the three evidence bodies, the modified basic probability allocation function of each of the three evidence bodies is weighted and averaged to obtain the basic probability allocation function of each of the three evidence bodies after weighted average. Then, the DS combination rule is applied to iteratively fuse the basic probability allocation function of each of the three evidence bodies after weighted average to obtain the fusion probability of each spatial grid cell. S125, determine the region division result of the spatial grid unit according to the fusion probability of each spatial grid unit, and generate the boundary parameters of the initial non-transmission region according to the region division result of the spatial grid unit; wherein, the region division result of the spatial grid unit includes the spatial grid unit belonging to the non-transmission region, the spatial grid unit not belonging to the non-transmission region, and the spatial grid unit not belonging to the non-transmission region.

5. The communication method for a UAV according to claim 4, characterized in that, It also includes step S126; Step 1S26: If the spatial grid cell region division result is uncertain whether the spatial grid cell belongs to the non-transmission area, the pilot UAV increases the collection frequency of environmental perception data and third-party system data, and re-executes steps S121 to S125.

6. The communication method for a UAV according to claim 4, characterized in that, In S125, the boundary parameters of the initial non-transmission region are generated based on the region division results of the spatial grid cells, including: S1251, mark the spatial grid cells whose region division results belong to the non-transmission area as valid non-transmission areas; S1252, the controlled area plane boundary is obtained according to the third-party system data, the union of the effective non-transmission area and the controlled area plane boundary is used as the initial vertex set, and the Graham scan method is used to generate a convex polygon as the plane boundary. S1253, Obtain the height range data of the controlled area based on the data from the third-party system, take the union of the height range data of the effective non-transmission area and the controlled area as the basic height range, and generate the height boundary according to a preset dynamic interference threshold; the preset dynamic interference threshold is dynamically set according to the height layer and the scene of the effective non-transmission area. S1254, determine the earliest value of the effective time and the latest value of the expiration time based on the data from the third-party system, take the earliest value of the effective time as the effective start time of the time boundary, and take the latest value of the expiration time as the expiration time of the time boundary; S1255, Generate the initial non-transmission zone boundary parameters based on the plane boundary, the height boundary, the effective start time of the time boundary, and the failure time of the time boundary.

7. The communication method for a UAV according to claim 2, characterized in that, S13 includes: in response to the occurrence of any one of at least one type of preset event, updating the boundary parameters of the initial non-transmission zone in real time, and determining the real-time boundary parameters of the non-transmission zone; wherein, the at least one type of preset event includes: the appearance of a new interference source, the change of the position of an existing interference source, the update of data from a third-party system, and a decrease in the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission zone.

8. The communication method for a UAV according to claim 7, characterized in that, The real-time updating of the boundary parameters of the initial non-transmission area includes: S131, Increase the collection frequency of environmental perception data and third-party system data, and perform data fusion based on the latest environmental perception data and third-party system data obtained after increasing the collection frequency to generate updated boundary parameters of the non-transmission zone; S132, compare the updated boundary parameters of the non-transmission region with the initial boundary parameters of the non-transmission region; if the comparison result meets the preset update conditions, then the updated boundary parameters of the non-transmission region are used as the real-time boundary parameters of the non-transmission region; otherwise, the initial boundary parameters of the non-transmission region are used as the real-time boundary parameters of the non-transmission region; the preset update conditions include: the planar boundary overlap rate is less than 80%, the height range variation exceeds 20 meters, and the probability that the spatial grid cell corresponding to the boundary region belongs to the non-transmission region decreases by more than 0.

1.

9. The communication method for a UAV according to claim 1, characterized in that, S4 includes: S41, calculate the position consistency score and the height consistency score based on the local determination result of the position of the first slave drone and the relative position observation values ​​of each slave drone among the at least one adjacent slave drone to the first slave drone. S42, based on the local determination results of the positions of each of the at least one adjacent drone, the proportion of the number of drones whose determination results are located in the non-transmission area is statistically analyzed, and a determination consistency score is calculated based on the proportion. S43, the position consistency score, the height consistency score and the determination consistency score are weighted and summed to obtain the collaborative consistency score.

10. The communication method for a UAV according to claim 1, characterized in that, The preset threshold includes a first preset threshold and a second preset threshold; the collaborative verification result instruction includes a first collaborative verification result instruction, a second collaborative verification result instruction, and a third collaborative verification result instruction; S5 includes: If the collaborative consistency score is greater than or equal to the first preset threshold, a first collaborative verification result instruction is generated; the first collaborative verification result instruction is used to indicate that the first slave drone is located in a non-transmission area. If the collaborative consistency score is greater than or equal to the second preset threshold, but less than the first preset threshold, a second collaborative verification result instruction is generated. The second collaborative verification result instruction is used to instruct the first UAV to increase the positioning sampling frequency and re-report the judgment result after a preset time. If the collaborative consistency score is less than the second preset threshold, a third collaborative verification result instruction is generated; the third collaborative verification result instruction is used to instruct the first UAV to enable RTK differential positioning to recalculate the position and re-report the judgment result.

11. The communication method for a UAV according to claim 10, characterized in that, S5 further includes: in response to receiving data after the first slave drone re-reports the judgment result based on the second collaborative verification result instruction and the third collaborative verification result instruction, recalculating the collaborative consistency score; if the recalculated collaborative consistency score is still less than the second preset threshold, generating and reporting device abnormal information.

12. The communication method for a UAV according to claim 1, characterized in that, The hierarchical dynamic transmission mechanism includes: for emergency data, regardless of whether the first slave drone is located in a non-transmission zone, the currently ongoing non-emergency transmission task is interrupted, and the emergency data is reported in real time via an ultra-wideband (UWB) link using frequency hopping technology; for important data, if the first slave drone is located in a non-transmission zone, the important data is cached in local storage; if the first slave drone is not located in a non-transmission zone, the cached important data is reported via a 5G link; for ordinary data, it is compressed and cached using a preset compression algorithm, and uploaded via a narrowband Internet of Things (NB-IoT) link during a preset low-power communication period.

13. The communication method for a UAV according to claim 1, characterized in that, Also includes: S6, obtain the current flight scenario type, and determine the number of each slave drone in the at least one adjacent slave drone and the preset threshold according to the current flight scenario type; the scenario type includes dense urban areas, open areas or emergency rescue areas.

14. A communication device for an unmanned aerial vehicle (UAV), characterized in that, Applications to pilot drones include: The real-time boundary parameter determination module for the non-transmission zone is used to determine the real-time boundary parameters of the non-transmission zone and send the real-time boundary parameters of the non-transmission zone to each of the slave drones in at least one slave drone. A data request frame sending module is configured to, in response to receiving a pending confirmation signal from a first slave drone, send a data request frame to each of at least one neighboring slave drones, so that each of the at least one neighboring slave drones can perceive the position of the first slave drone based on the data request frame and report neighbor perception data; the first slave drone is one of the at least one slave drones; the pending confirmation signal is sent by the first slave drone after N consecutive local determinations that the first slave drone is located in a non-transmission area; the local determination result is determined by the first slave drone after acquiring its own state perception data based on... The determination of whether a device is located within a dynamic non-transmission zone is obtained by using real-time boundary parameters of the non-transmission zone and the state-aware data. The state-aware data includes at least position data and time data. The signal to be confirmed includes the local determination result and the state-aware data. N is a positive integer. Each of the at least one neighboring drones is a drone that is positionally adjacent to the first drone. The neighbor-aware data includes the relative position observations and signal directions of each of the at least one neighboring drones relative to the first drone, as well as the local determination results of the position of each of the at least one neighboring drones. A data receiving module is used to receive neighbor perception data from each of the at least one adjacent slave drone; The collaborative consistency score calculation module is used to calculate the collaborative consistency score based on the signal to be confirmed and the neighbor perception data from each of the at least one neighboring drone. The collaborative verification result instruction generation module is used to compare the collaborative consistency score with a preset threshold, generate a collaborative verification result instruction, and send the collaborative verification result instruction to the first slave drone, so that the first slave drone can determine the final regional affiliation status according to the collaborative verification result instruction, and perform data transmission according to the final regional affiliation status in accordance with the hierarchical dynamic transmission mechanism.

15. A communication method for an unmanned aerial vehicle (UAV), characterized in that, Applied to a first slave drone, the first slave drone being one of at least one slave drone; the method includes: A1 receives real-time boundary parameters from the non-transmission zone of the pilot drone; A2, acquire the first state perception data from the UAV itself, the state perception data including at least location data and time data; A3. Determine whether it is located in the dynamic non-transmission zone based on the real-time boundary parameters of the non-transmission zone and the state-aware data, and obtain the local determination result. A4, when the number of times the local determination result indicates that the first slave drone is located in the dynamic non-transmission zone is greater than N, a confirmation signal is sent to the lead drone, so that the lead drone sends a data request frame to each of the at least one neighboring slave drones, so that each of the at least one neighboring slave drones perceives the position of the first slave drone according to the data request frame, reports neighbor perception data, calculates a cooperative consistency score based on the confirmation signal and the neighbor perception data from each of the at least one neighboring slave drones, compares the cooperative consistency score with a preset threshold, generates a cooperative verification result instruction, and issues the cooperative verification result instruction to the first slave drone; the confirmation signal includes the local determination result and the state perception data; N is a positive integer; each of the at least one neighboring slave drones is a slave drone that is positionally adjacent to the first slave drone; the neighbor perception data includes the relative position observation value and signal direction of each of the at least one neighboring slave drones to the first slave drone, and the local determination result of the position of each of the at least one neighboring slave drones itself; A5 receives the collaborative verification result instruction from the lead drone, determines the final regional affiliation status based on the collaborative verification result instruction, and transmits data according to the hierarchical dynamic transmission mechanism based on the final regional affiliation status.

16. A communication device for an unmanned aerial vehicle (UAV), characterized in that, Applied to a first slave drone, wherein the first slave drone is one of at least one slave drone; The communication device includes: The boundary parameter receiving module is used to receive real-time boundary parameters from the non-transmission area of ​​the pilot drone; The perception module is used to acquire first state perception data from the UAV itself, the state perception data including at least position data and time data; The local determination module is used to determine whether it is located in the dynamic non-transmission zone based on the real-time boundary parameters of the non-transmission zone and the state awareness data, and to obtain the local determination result. The pending confirmation signal sending module is used to send a pending confirmation signal to the lead drone when the number of times the local determination result indicates that the first slave drone is located in the dynamic non-transmission zone is greater than N. This causes the lead drone to send a data request frame to each of the at least one neighboring slave drones, enabling each of the at least one neighboring slave drones to perceive the position of the first slave drone based on the data request frame, report neighbor perception data, calculate a cooperative consistency score based on the pending confirmation signal and the neighbor perception data from each of the at least one neighboring slave drones, compare the cooperative consistency score with a preset threshold, generate a cooperative verification result instruction, and issue the cooperative verification result instruction to the first slave drone. N is a positive integer; each of the at least one neighboring slave drones is a slave drone that is geographically adjacent to the first slave drone; the neighbor perception data includes the relative position observation value and signal direction of each of the at least one neighboring slave drones to the first slave drone, as well as the local determination result of the position of each of the at least one neighboring slave drones. The collaborative verification result instruction receiving module is used to receive collaborative verification result instructions from the pilot UAV, determine the final regional affiliation status based on the collaborative verification result instructions, and transmit data according to the final regional affiliation status in accordance with a hierarchical dynamic transmission mechanism.

17. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform the communication method of the UAV as claimed in any one of claims 1-13 or 15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the communication method of the UAV as described in any one of claims 1-13 or 15.

19. A computer program product, characterized in that, Includes computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the communication method of the UAV as claimed in any one of claims 1-13 or 15.

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