Method and apparatus for determining frequency hopping strategy of unmanned aerial vehicle, device, medium, and product

Abstract

A method and apparatus for determining a frequency hopping strategy of an unmanned aerial vehicle, a device, a medium, and a product. The method comprises: during operation of a target unmanned aerial vehicle, acquiring measurement data of related nodes associated with the target unmanned aerial vehicle at every set time interval (S110); analyzing the measurement data to obtain analysis results, and on the basis of the analysis results, determining a current signal scenario of the target unmanned aerial vehicle (S120), wherein the current signal scenario comprises at least one of the following: a multi-path effect, dynamic interference and signal fading; and determining a target frequency hopping strategy, which matches the current signal scenario, of the target unmanned aerial vehicle, and feeding back a target frequency point matching the target frequency hopping strategy to the target unmanned aerial vehicle (S130).

Description

Methods, devices, equipment, media, and products for determining frequency hopping strategies for unmanned aerial vehicles (UAVs).

[0001] This application claims priority to Chinese Patent Application No. 202510195419.5, filed with the Chinese Patent Office on February 21, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of wireless communication technology, and for example to a method, apparatus, device, medium and product for determining frequency hopping strategy for unmanned aerial vehicles (UAVs). Background Technology

[0003] A star network is a network topology characterized by all nodes connected through a central node, forming a star-shaped distribution. It offers advantages such as simple structure, ease of control, easy link establishment, and low network latency.

[0004] Because drones have a high speed and fly in complex environments, they are easily affected by base stations, signal towers or other wireless interference sources in actual tests. How to solve the problem that traditional frequency hopping strategies in star networks cannot adapt to the drone environment and cause image transmission lag is a key research issue in the industry. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for determining frequency hopping strategies for unmanned aerial vehicles (UAVs) to solve the problem that traditional frequency hopping strategies in star networks cannot adapt to the UAV environment, resulting in image transmission lag. It can quickly and accurately determine the target frequency point that matches the current flight environment of the UAV, thereby improving the operational stability of the UAV.

[0006] According to one aspect of this application, a method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) is provided, the method being executed by a central node in a star network, the method comprising:

[0007] During the operation of the target drone, measurement data of each relevant node associated with the target drone is acquired at set time intervals;

[0008] The measurement data are analyzed to obtain analysis results, and the current signal scene of the target UAV is determined based on the analysis results; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading;

[0009] Determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0010] According to another aspect of this application, a frequency hopping strategy determination device for unmanned aerial vehicles (UAVs) is provided. This device is configured at a central node in a star network and includes:

[0011] The measurement data acquisition module is configured to acquire measurement data of each relevant node associated with the target drone at set time intervals during the operation of the target drone.

[0012] The current signal scene determination module is configured to analyze each of the measurement data, obtain each analysis result, and determine the current signal scene of the target UAV based on each of the analysis results; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading;

[0013] The target frequency hopping strategy determination module is configured to determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and to feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0014] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the frequency hopping strategy determination method for a UAV according to any embodiment of this application.

[0018] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the frequency hopping strategy determination method for a UAV according to any embodiment of this application.

[0019] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the frequency hopping strategy determination method for unmanned aerial vehicles (UAVs) according to any embodiment of this application. Attached Figure Description

[0020] Figure 1 is a flowchart of a method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to Embodiment 1 of this application;

[0021] Figure 2 is a flowchart of a method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to Embodiment 2 of this application;

[0022] Figure 3 is a flowchart of another method for determining the frequency hopping strategy of a UAV according to Embodiment 2 of this application;

[0023] Figure 4 is a schematic diagram of a frequency hopping strategy determination device for an unmanned aerial vehicle (UAV) according to Embodiment 3 of this application;

[0024] Figure 5 is a schematic diagram of the structure of an electronic device that implements the frequency hopping strategy determination method for UAVs according to an embodiment of this application. Detailed Implementation

[0025] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, for example, including, in addition to a process, method, system, product, or apparatus comprising a series of steps or units, processes, methods, systems, products, or apparatuses that do not explicitly list such series of steps or units, or other steps or units inherent to such processes, methods, products, or apparatuses.

[0026] Example 1

[0027] Figure 1 is a flowchart of a frequency hopping strategy determination method for a drone according to Embodiment 1 of this application. This embodiment is applicable to star network architectures where target drones face complex signal scenarios such as multipath effects, dynamic interference, and signal fading when performing various operations such as line fault detection, performances, and pesticide spraying. This leads to insufficient adaptability of traditional frequency hopping strategies and image transmission lag. This method can be executed by a drone frequency hopping strategy determination device, which can be implemented in hardware and / or software. The drone frequency hopping strategy determination device can be configured in the central node of the star network. The central node can be a computer, server, cloud service platform, or dedicated communication equipment, etc. The type of central node is not limited in this embodiment. As shown in Figure 1, the method includes:

[0028] S110. During the operation of the target UAV, measurement data of each relevant node associated with the target UAV is acquired at set time intervals.

[0029] The target drone can be one or more drones performing tasks such as line fault detection, performances, or spraying chemicals.

[0030] In this embodiment, each related node associated with the target drone can be any node in a star network, such as the first node, the second node, or the central node that interacts with the target drone, and includes the target drone itself.

[0031] Optionally, in this embodiment, the central node (core node) in the star network can periodically acquire measurement data of each related node associated with the target drone during the operation of the target drone; for example, the central node can acquire measurement data of each related node associated with the target drone every five minutes, ten minutes or one hour.

[0032] The measurement data obtained in this embodiment may include: the Received Signal Strength Indicator (RSSI) and path loss of the target UAV, the periodic Channel Quality Indicator (CQI) of the target UAV, the noise value of the target UAV, the noise value and signal-to-noise ratio (SNR) of the central node, the moving speed of the target UAV, and the center frequency.

[0033] In this embodiment, the acquisition of each measurement data is done with user authorization, and the acquisition method is reasonable and legal.

[0034] S120. Analyze each of the measurement data to obtain each analysis result, and determine the current signal scene of the target UAV based on each of the analysis results.

[0035] Current signal scenarios include at least one of the following: multipath effects, dynamic interference, and signal fading.

[0036] Optionally, in this embodiment, after obtaining the measurement data of each relevant node associated with the target UAV, the measurement data can be analyzed to obtain multiple measurement data analysis results. For example, the analysis results may include RSSI fluctuation, path loss fluctuation, periodic CQI fluctuation of the target UAV and changes in noise value, changes in noise value of the central node and fluctuation of signal-to-noise ratio, or frequency offset of the target UAV, etc.

[0037] In some optional implementations of this embodiment, after obtaining the analysis results, the current signal scenario of the target UAV can be determined based on the analysis results. In this embodiment, the current signal scenario can be one or more of the following: the direct path is blocked by buildings or other objects, or multipath effects occur; the channel environment is unstable due to dynamic random interference; or bit errors and signal fading are caused by Doppler shift due to high-speed movement.

[0038] The essence of multipath propagation is that a signal travels through multiple paths to the receiver, each with varying lengths and attenuation levels. This results in differences in amplitude and phase of the signal components arriving at the receiver. These components cause destructive interference at the receiver, weakening or even completely canceling out the signal strength, leading to significant fluctuations in signal strength and path loss. To determine whether multipath propagation is the cause of these fluctuations, the RSSI and path loss changes in the measurements at any node can be monitored.

[0039] Dynamic interference is a common scenario with a wide range of causes. One common scenario is interference from wireless signals or public network base stations in urban areas. To address this, one can continuously monitor the periodic CQI feedback from the access node (i.e., the target drone) to assess changes in its CQI and noise levels, as well as the noise fluctuations and SNR of the master control node (i.e., the central node).

[0040] In this embodiment, the formula for calculating a single Doppler shift can be simplified as follows: Where v represents the target drone's moving speed in meters per second (m / s), freq represents the center frequency in Hertz (Hz), and c represents the speed of light; from the above formula, assuming the drone's moving speed is 20 m / s, the center frequency is... At Hz, the single octave deviation is approximately Hz, double offset is 773.34Hz.

[0041] Typically, a Physical Random Access Channel (PRACH) with a 1250Hz subcarrier spacing supports a frequency offset range of 625Hz. However, based on the above estimation, when the operating frequency is high, a movement speed of only 20m / s may cause the frequency offset to exceed the channel's estimated tolerance. This could lead to a situation where, in certain circumstances, such as a communication link loss, a remote controller restart, or an aircraft being shot down by jamming, the aircraft and the remote controller may be unable to reconnect and restore communication.

[0042] In some optional implementations of this embodiment, a correspondence between each analysis result and each signal scenario can be established in advance, and the current signal scenario corresponding to the current analysis result can be determined according to the correspondence. This can quickly determine the current signal scenario and provide a basis for determining the optimal target frequency hopping strategy.

[0043] S130. Determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0044] Optionally, in this embodiment, after determining the current signal scene of the target drone, a target frequency hopping strategy matching the target drone can be determined based on the current signal scene, and a target frequency point can be determined based on the target frequency hopping strategy. The target frequency point is then fed back to the target drone so that the target drone adjusts its operating frequency point to the target frequency point.

[0045] Optionally, in this embodiment, determining the target frequency hopping strategy of the target UAV that matches the current signal scenario may include: in response to determining that the current signal scenario is a multipath effect, determining the target frequency hopping strategy as random, cross-frequency band hopping; or, in response to determining that the current signal scenario is dynamic interference, determining the first frequency point with the lowest overall noise level at the central node, and stopping hopping based on the first frequency point; or, in response to determining that the current signal scenario is signal fading, shifting the frequency band of the target UAV to a preset frequency band; the frequency of the preset frequency band is lower than the current frequency band.

[0046] In some optional implementations of this embodiment, if the current signal scenario is determined to be a multipath effect, then the target frequency hopping strategy can be determined to be random, cross-band frequency hopping. In some implementations, by building different signal models, it can be found that the locations where fading occurs at different frequencies are quite different. In general, signal fluctuations caused by multipath effects can be improved by using frequency redundancy and prioritizing the use of wide-interval, cross-band frequency hopping.

[0047] For example, if the current signal scenario is determined to be a multipath effect, the current frequency point 24490 can be adjusted to the frequency point 14440, where the frequency point 14440 is the target frequency point involved in this embodiment.

[0048] In some alternative implementations of this embodiment, for scenarios with dynamic random interference, frequency hopping is used to find a frequency point with a relatively stable channel environment. The first frequency point with the lowest overall noise level is determined at the central node. For example, the sum of the noise values ​​of the central node and the noise values ​​of related nodes can be used directly to select a relatively small value as the stop hopping frequency point.

[0049] In some alternative implementations of this embodiment, frequency selection is performed for high-speed moving scenarios. As can be seen from the calculation formula of single Doppler offset, in scenarios with constant speed, the lower the operating center frequency, the lower the Doppler frequency offset. Therefore, in scenarios where frequency hopping is found to be caused by excessive frequency offset, the frequency hopping should be stopped by selecting a lower frequency across the frequency band.

[0050] In this embodiment, during the operation of the target UAV, the central node in the star network acquires measurement data of each related node associated with the target UAV at set time intervals. The measurement data is analyzed to obtain analysis results, and the current signal scene of the target UAV is determined based on the analysis results. The current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading. The target frequency hopping strategy of the target UAV that matches the current signal scene is determined, and the target frequency point that matches the target frequency hopping strategy is fed back to the target UAV. This solves the problem that the traditional frequency hopping strategy in the star network cannot adapt to the UAV environment, resulting in image transmission lag. It can quickly and accurately determine the target frequency point that matches the current flight environment of the UAV, thus improving the operational stability of the UAV.

[0051] Example 2

[0052] Figure 2 is a flowchart of a method for determining the frequency hopping strategy of a UAV according to Embodiment 2 of this application. This embodiment is an adjustment based on the embodiments above, and the scheme in this embodiment can be combined with the various optional schemes in one or more of the above embodiments. As shown in Figure 2, the method includes:

[0053] S210. During the operation of the target UAV, measurement data of each relevant node associated with the target UAV is acquired at set time intervals.

[0054] S220. Analyze the measurement data to obtain the analysis results.

[0055] Optionally, in this embodiment, analyzing the measurement data to obtain the analysis results may include: calculating the absolute value of the RSSI difference between the current period and the previous period, and the absolute value of the path loss difference, to obtain a first analysis result characterizing the RSSI fluctuation and path loss fluctuation; determining the periodic CQI fluctuation of the target UAV, the change in noise value, the change in noise value of the central node, and the fluctuation of the signal-to-noise ratio, to obtain a second analysis result; and determining the frequency offset of the target UAV based on the moving speed and center frequency, to obtain a third analysis result.

[0056] In some optional implementations of this embodiment, the difference between the RSSI and path loss of the current period and the RSSI and path loss of the previous period can be determined as the fluctuation of RSSI and path loss, thus obtaining the first analysis result.

[0057] In some alternative implementations of this embodiment, the difference between the periodic CQI and noise value of the target UAV in the current period, the noise value and signal-to-noise ratio of the central node and the periodic CQI and noise value of the target UAV in the previous period, the noise value and signal-to-noise ratio of the central node can be determined as the second analysis result.

[0058] In some alternative implementations of this embodiment, the obtained target drone's moving speed and center frequency can be substituted into the formula. In this process, the frequency offset of the target drone is obtained, which is the third analysis result involved in this embodiment; where v represents the moving speed of the target drone in m / s, freq represents the center frequency in Hz, and c represents the speed of light.

[0059] S230. Determine the current signal scene of the target UAV based on the analysis results.

[0060] Optionally, in this embodiment, determining the current signal scenario of the target UAV based on each analysis result may include: determining the current signal scenario as a multipath effect in response to the determination, based on the first analysis result, that the absolute value of the RSSI difference and the absolute value of the path loss difference are both greater than or equal to their respective first preset fluctuation thresholds; determining the current signal scenario as dynamic interference in response to the determination, based on the second analysis result, that the amplitude of any one of the four parameters—the fluctuation of the periodic CQI of the target UAV, the change of the noise value of the target UAV, the change of the noise value of the central node, and the fluctuation of the SNR of the central node—is greater than or equal to the second preset fluctuation threshold corresponding to that parameter; and determining the current signal scenario as signal fading in response to the determination, based on the third analysis result, that the frequency offset of the target UAV is greater than or equal to a preset frequency offset threshold.

[0061] In this embodiment, the first set fluctuation threshold, the second set fluctuation threshold, and the set frequency offset threshold can all be adaptively set according to the actual application scenario of the target UAV (such as flight speed, communication environment interference intensity) or the system communication performance index requirements (such as link synchronization success rate, anti-frequency offset capability).

[0062] In some optional implementations of this embodiment, if the absolute values ​​of the differences between the RSSI and path loss of the current period and those of the previous period are both greater than or equal to their respective first preset fluctuation thresholds, then the current signal scenario can be determined to be a multipath effect; if the absolute values ​​of the differences between the periodic CQI and noise value of the target UAV, the noise value of the central node, and the signal-to-noise ratio of the current period and those of the previous period are both greater than or equal to their respective second preset fluctuation thresholds, then the current signal scenario can be determined to be dynamic interference; if the obtained moving speed of the target UAV and the center frequency are substituted into the formula... If the frequency offset of the target drone is greater than or equal to the set frequency offset threshold, then the current signal scenario is determined to be signal fading.

[0063] S240. Determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0064] S250, In response to the current signal scenario simultaneously including multipath effect, dynamic interference and signal fading, process multipath effect, dynamic interference and signal fading in sequence.

[0065] Optionally, in this embodiment, if it is determined that the current signal scenario simultaneously includes multipath effect, dynamic interference, and signal fading, then multipath effect, dynamic interference, and signal fading can be processed sequentially. That is, first select the frequency hopping strategy corresponding to the multipath effect, then select the frequency hopping strategy corresponding to the dynamic interference, and finally select the frequency hopping strategy corresponding to the signal fading.

[0066] To better understand the UAV frequency hopping strategy determination method involved in this embodiment, Figure 3 is a flowchart of another UAV frequency hopping strategy determination method provided according to Embodiment 2 of this application. The master node involved in Figure 3 is the central node in the star network involved in this embodiment, and the slave nodes involved in Figure 3 are the other nodes in the star network involved in this embodiment besides the central node. The solution of this embodiment can determine the current signal scene by monitoring the changes of different parameters within the monitoring period and match the corresponding stop-hop frequency selection strategy, thereby ensuring the stability of image transmission and data transmission during UAV flight.

[0067] In this embodiment, when two or three signal scenarios—multipath effect, dynamic interference, and signal fading—occur simultaneously, a priority is assigned to each scenario: multipath effect has the highest priority, followed by dynamic interference, and signal fading has the lowest priority. This priority setting maintains the communication quality of the current link, prioritizes energy usage, and ensures the stability of image transmission and data transmission during UAV flight.

[0068] Example 3

[0069] Figure 4 is a schematic diagram of a frequency hopping strategy determination device for an unmanned aerial vehicle (UAV) according to Embodiment 3 of this application. As shown in Figure 4, the device can be configured in the central node of a star network. The device includes: a measurement data acquisition module 410, a current signal scene determination module 420, and a target frequency hopping strategy determination module 430.

[0070] The measurement data acquisition module 410 is configured to acquire measurement data of each relevant node associated with the target drone at set time intervals during the operation of the target drone.

[0071] The current signal scene determination module 420 is configured to analyze each of the measurement data, obtain each analysis result, and determine the current signal scene of the target UAV based on each of the analysis results; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading;

[0072] The target frequency hopping strategy determination module 430 is configured to determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and to feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0073] In this embodiment, the measurement data acquisition module acquires measurement data of each relevant node associated with the target UAV at set time intervals during the operation of the target UAV. The current signal scene determination module analyzes the measurement data to obtain analysis results and determines the current signal scene of the target UAV based on the analysis results. The target frequency hopping strategy determination module determines the target frequency hopping strategy of the target UAV that matches the current signal scene and feeds back the target frequency point that matches the target frequency hopping strategy to the target UAV. This solves the problem that traditional frequency hopping strategies in star networks cannot adapt to the UAV environment, resulting in image transmission lag. It can quickly and accurately determine the target frequency point that matches the current flight environment of the UAV, thus improving the operational stability of the UAV.

[0074] In some optional implementations of this embodiment, the measurement data includes:

[0075] The received signal strength indication and path loss of the target UAV, the periodic channel quality indication and noise value of the target UAV, the noise value and signal-to-noise ratio of the central node, the moving speed of the target UAV, and the center frequency.

[0076] In some optional implementations of this embodiment, the current signal scene determination module 420 is configured to analyze each of the measurement data in the following manner to obtain each analysis result:

[0077] Calculate the absolute value of the RSSI difference between the current period and the previous period, and the absolute value of the path loss difference, to obtain the first analysis result characterizing the RSSI fluctuation and the path loss fluctuation.

[0078] The second analysis result is obtained by determining the periodic CQI fluctuations and noise value changes of the target UAV, the noise value changes of the central node, and the signal-to-noise ratio fluctuations.

[0079] The frequency offset of the target UAV is determined based on its moving speed and center frequency, resulting in a third analysis result.

[0080] In some optional implementations of this embodiment, the current signal scene determination module 420 is configured to determine the current signal scene of the target UAV based on the analysis results in the following manner:

[0081] In response to the determination based on the first analysis result that the absolute value of the RSSI difference and the absolute value of the path loss difference are both greater than or equal to their respective first set fluctuation thresholds, the current signal scenario is determined to be a multipath effect;

[0082] In response to the determination based on the second analysis result that, among the four parameters—periodic CQI fluctuation of the target UAV, noise value change of the target UAV, noise value change of the central node, and SNR fluctuation of the central node—the amplitude of any one parameter is greater than or equal to the second preset fluctuation threshold corresponding to that parameter, the current signal scenario is determined to be dynamic interference.

[0083] In response to determining, based on the third analysis result, that the frequency offset of the target UAV is greater than or equal to a set frequency offset threshold, the current signal scenario is determined to be signal fading.

[0084] In some optional implementations of this embodiment, the target frequency hopping strategy determination module 430 is configured as follows:

[0085] In response to determining that the current signal scenario is a multipath effect, the target frequency hopping strategy is determined to be random, cross-band frequency hopping;

[0086] or,

[0087] In response to determining that the current signal scenario is dynamic interference, the first frequency point with the lowest overall noise level is determined at the central node, and the stop-hopping is performed based on the first frequency point;

[0088] or,

[0089] In response to determining that the current signal scenario is a signal fading, the frequency band of the target UAV is switched to a preset frequency band; the frequency of the preset frequency band is lower than that of the current frequency band.

[0090] In some optional implementations of this embodiment, the device further includes: a priority determination module, configured to process multipath effect, dynamic interference and signal fading in sequence in response to the current signal scenario simultaneously including multipath effect, dynamic interference and signal fading.

[0091] The frequency hopping strategy determination device for UAVs provided in this application embodiment can execute the frequency hopping strategy determination method for UAVs provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0092] Example 4

[0093] Figure 5 shows a schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of this application. The electronic device represents various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples.

[0094] As shown in Figure 5, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for determining the frequency hopping strategy of a UAV, which includes: acquiring measurement data of each relevant node associated with the target UAV at set time intervals during the operation of the target UAV; analyzing each measurement data to obtain each analysis result, and determining the current signal scene of the target UAV based on each analysis result; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading; determining a target frequency hopping strategy of the target UAV that matches the current signal scene, and feeding back the target frequency point that matches the target frequency hopping strategy to the target UAV.

[0097] In some embodiments, the UAV frequency hopping strategy determination method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the UAV frequency hopping strategy determination method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the UAV frequency hopping strategy determination method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. Examples of machine-readable storage media may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disc-read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT), liquid crystal display (LCD), or monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0103] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0104] It should be understood that the various processes shown above can be used to rearrange, add, or delete steps. For example, the multiple steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0105] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the frequency hopping strategy determination method for unmanned aerial vehicles as provided in any embodiment of this application.

[0106] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed 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 remote computers, the remote computer can be connected to the user's computer via any type of network such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

Claims

1. A method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV), executed by a central node in a star network, the method comprising: During the operation of the target drone, measurement data of each relevant node associated with the target drone is acquired at set time intervals. The measurement data are analyzed to obtain analysis results, and the current signal scene of the target UAV is determined based on the analysis results; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading; Determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

2. The method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to claim 1, wherein, The measurement data includes: The received signal strength index (RSSI) and path loss of the target UAV, the periodic channel quality index (CQI) and noise value of the target UAV, the noise value and signal-to-noise ratio (SNR) of the central node, the moving speed of the target UAV, and the center frequency.

3. The method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to claim 2, wherein, The analysis of each measurement data to obtain various analysis results includes: Calculate the absolute value of the RSSI difference between the current period and the previous period, and the absolute value of the path loss difference, to obtain the first analysis result characterizing the RSSI fluctuation and the path loss fluctuation. The second analysis result is obtained by determining the periodic CQI fluctuations and noise value changes of the target UAV, the noise value changes of the central node, and the SNR fluctuations. The frequency offset of the target UAV is determined based on its moving speed and center frequency, resulting in a third analysis result.

4. The method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to claim 3, wherein, Determining the current signal scene of the target UAV based on the analysis results includes: In response to the determination based on the first analysis result that the absolute value of the RSSI difference and the absolute value of the path loss difference are both greater than or equal to their respective first set fluctuation thresholds, the current signal scenario is determined to be a multipath effect; In response to the determination based on the second analysis result that, among the four parameters—periodic CQI fluctuation of the target UAV, noise value change of the target UAV, noise value change of the central node, and SNR fluctuation of the central node—the amplitude of any one parameter is greater than or equal to the second preset fluctuation threshold corresponding to that parameter, the current signal scenario is determined to be dynamic interference. In response to determining, based on the third analysis result, that the frequency offset of the target UAV is greater than or equal to a set frequency offset threshold, the current signal scenario is determined to be signal fading.

5. The method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to claim 1, wherein, The target frequency hopping strategy for determining the target UAV that matches the current signal scene includes: In response to determining that the current signal scenario is a multipath effect, the target frequency hopping strategy is determined to be random, cross-band frequency hopping; or, In response to determining that the current signal scenario is dynamic interference, the first frequency point with the lowest overall noise level is determined at the central node, and the stop-hopping is performed based on the first frequency point; or, In response to determining that the current signal scenario is a signal fading, the frequency band of the target UAV is switched to a preset frequency band; the frequency of the preset frequency band is lower than that of the current frequency band.

6. The method for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV) according to claim 1, the method further includes: In response to the current signal scenario including multipath effects, dynamic interference, and signal fading, multipath effects, dynamic interference, and signal fading are processed sequentially.

7. A frequency hopping strategy determination device for unmanned aerial vehicles (UAVs), configured at the central node in a star network, the device comprising: The measurement data acquisition module is configured to acquire measurement data of each relevant node associated with the target drone at set time intervals during the operation of the target drone. The current signal scene determination module is configured to analyze each of the measurement data, obtain each analysis result, and determine the current signal scene of the target UAV based on each of the analysis results; the current signal scene includes at least one of the following: multipath effect, dynamic interference, and signal fading; The target frequency hopping strategy determination module is configured to determine the target frequency hopping strategy of the target UAV that matches the current signal scenario, and to feed back the target frequency point that matches the target frequency hopping strategy to the target UAV.

8. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the frequency hopping strategy determination method for the UAV according to any one of claims 1-6.

9. A computer-readable storage medium storing computer instructions for causing a processor to execute the frequency hopping strategy determination method for an unmanned aerial vehicle according to any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements a frequency hopping strategy determination method for an unmanned aerial vehicle according to any one of claims 1-6.

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