Frequency hopping strategy determination method and device of unmanned aerial vehicle, equipment, medium and product

Abstract

The embodiment of the invention discloses a frequency hopping strategy determination method and device for an unmanned aerial vehicle, equipment, a medium and a product, and relates to the technical field of wireless communication. The method comprises the following steps: in the operation process of a target unmanned aerial vehicle, obtaining measurement data of each related node associated with the target unmanned aerial vehicle every set time period; analyzing each measurement data to obtain each analysis result, and determining a current signal scene of the target unmanned aerial vehicle based on each analysis result; the current signal scene comprises at least one of multipath effect, dynamic interference and signal fading; the target frequency hopping strategy of the target unmanned aerial vehicle matched with the current signal scene is determined, and the target frequency point matched with the target frequency hopping strategy is fed back to the target unmanned aerial vehicle, so that the problem of image transmission lagging caused by the fact that a traditional frequency hopping strategy in a star network cannot adapt to the unmanned aerial vehicle environment is solved; the target frequency point matched with the current flight environment of the unmanned aerial vehicle can be quickly and accurately determined, and the operation stability of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method, device, equipment, medium and product for determining a frequency hopping strategy of an unmanned aerial vehicle. Background Art

[0002] Star network is a network topology, which is characterized by each node device in the network is connected through a clear central node, showing a star-shaped distribution feature. It has the advantages of simple structure, easy control, easy link establishment, and low network delay.

[0003] Since drones move at a relatively fast speed and have a relatively complex flying environment, they are easily affected by base stations, signal towers or other wireless interference sources in actual tests. How to solve the problem that the traditional frequency hopping strategy in the star network cannot adapt to the drone environment, resulting in image transmission jams, is a key issue in internal research. Summary of the invention

[0004] The present invention provides a method, device, equipment, medium and product for determining the frequency hopping strategy of an unmanned aerial vehicle (UAV), so as to solve the problem that the traditional frequency hopping strategy in a star network cannot adapt to the UAV environment, resulting in image transmission jams. The method can quickly and accurately determine the target frequency point that matches the current flight environment of the UAV, thereby improving the operation stability of the UAV.

[0005] According to one aspect of the present invention, a method for determining a frequency hopping strategy of a drone is provided, the method being executed by a central node in a star network, the method comprising:

[0006] During the operation of the target UAV, the measurement data of each relevant node associated with the target UAV is obtained at each set time period;

[0007] 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; the current signal scene includes at least one of the following: multipath effect, dynamic interference and signal fading;

[0008] A target frequency hopping strategy of the target UAV that matches the current signal scenario is determined, and a target frequency point that matches the target frequency hopping strategy is fed back to the target UAV.

[0009] According to another aspect of the present invention, a device for determining a frequency hopping strategy of a drone is provided, the device being configured at a central node in a star network, the device comprising:

[0010] A measurement data acquisition module, used to acquire measurement data of each relevant node associated with the target UAV at set time intervals during the operation of the target UAV;

[0011] A current signal scene determination module, used to 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; the current signal scene includes at least one of the following: multipath effect, dynamic interference and signal fading;

[0012] The target frequency hopping strategy determination module is used to 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.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the frequency hopping strategy of the drone described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the frequency hopping strategy of a drone as described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the method for determining the frequency hopping strategy of a drone according to any embodiment of the present invention.

[0019] The technical solution of the embodiment of the present invention is to obtain the measurement data of each relevant node associated with the target UAV at a set time period during the operation of the target UAV through the central node in the star network; 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; 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 scene, and feed back the target frequency matching the target frequency hopping strategy to the target UAV, which solves the problem that the traditional frequency hopping strategy in the star network cannot adapt to the UAV environment, resulting in image transmission jamming, and can quickly and accurately determine the target frequency matching the current flight environment of the UAV, thereby improving the operation stability of the UAV.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for determining a frequency hopping strategy of a drone provided in accordance with Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a method for determining a frequency hopping strategy of a drone provided in accordance with Embodiment 2 of the present invention;

[0024] Figure 3 is a flow chart of another method for determining a frequency hopping strategy of a UAV provided in accordance with Embodiment 2 of the present invention;

[0025] Figure 4 is a schematic diagram of the structure of a device for determining a frequency hopping strategy of a drone provided in accordance with Embodiment 3 of the present invention;

[0026] Figure 5 The present invention is a schematic diagram of the structure of an electronic device for implementing the method for determining the frequency hopping strategy of a drone according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a method for determining a frequency hopping strategy of a drone according to the first embodiment of the present invention. This embodiment is applicable to certain situations. This method can be executed by a frequency hopping strategy determining device of a drone. The frequency hopping strategy determining device of the drone can be implemented in the form of hardware and / or software. The frequency hopping strategy determining device of the drone can be configured in a central node in a star network. The central node can be a computer, a server, a cloud service platform or a dedicated communication device, etc. This embodiment does not limit it. Figure 1 As shown, the method includes:

[0031] Step 110: During the operation of the target UAV, measurement data of each relevant node associated with the target UAV is obtained at set time intervals.

[0032] The target UAV may be one or more UAVs that are performing operations such as line fault detection, performance, or spraying of medicine, which is not limited in this embodiment.

[0033] In this embodiment, each relevant node associated with the target UAV can be any node in the star network, for example, the first node, the second node or the central node for data interaction with the target UAV, which is not limited in this embodiment.

[0034] Optionally, in this embodiment, the central node (core node) in the star network can periodically obtain measurement data of each relevant node associated with the target UAV during the operation of the target UAV; for example, the measurement data of each relevant node associated with the target UAV can be obtained every five minutes, ten minutes or one hour.

[0035] The measurement data obtained in this embodiment may include: received signal strength indication (RSSI), path loss, periodic channel quality indicator (CQI) feedback of the target UAV, changes in noise values, noise changes and signal-to-noise ratio (SNR) of the central node, speed of the target UAV, and center frequency.

[0036] In this embodiment, each measurement data is obtained after authorization by the user, and the acquisition method is reasonable and legal.

[0037] Step 120: 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.

[0038] The current signal scenario includes at least one of the following: multipath effect, dynamic interference and signal fading.

[0039] Optionally, in this embodiment, after obtaining the measurement data of each relevant node associated with the target UAV, each measurement data can be further analyzed to obtain multiple measurement data analysis results. For example, each analysis result can be RSSI fluctuations, path loss fluctuations, CQI feedback of the target UAV and changes in noise values, noise of the central node and fluctuations in the signal-to-noise ratio, or frequency deviation of the target UAV, which are not limited in this embodiment.

[0040] In an optional implementation of the present embodiment, after obtaining the analysis results, the current signal scene of the target UAV can be further determined based on the analysis results; in the present embodiment, the current signal scene may be one or more of the following: the direct path is blocked by buildings, etc. or a multipath effect occurs, the channel environment is unstable due to dynamic random interference, or high-speed movement produces Doppler shift, resulting in bit errors and signal fading.

[0041] It is understandable that the essence of the multipath effect is that the signal propagates to the receiving end through multiple paths. The length and attenuation amplitude of each path are different, resulting in differences in amplitude and phase of the signal components reaching the receiving end. These components produce destructive interference at the receiving end, which weakens the signal strength or even completely cancels it out, resulting in more obvious fluctuations in signal strength and signal transmission path loss. Based on its characteristics, we can determine whether the channel environment fluctuation is caused by multipath by monitoring the RSSI in the measurement results of any node and the change in path loss.

[0042] Dynamic interference is a common scenario, and its causes are quite diverse. One common scenario is interference from wireless signals or public network base stations in urban areas. For this scenario, we can continuously monitor the periodic CQI feedback of the access node, determine the changes in the CQI and noise values ​​carried therein, as well as the noise fluctuations and SNR of the master node itself, and make a comprehensive consideration.

[0043] In this embodiment, the calculation formula of the single Doppler shift can be simplified as follows: Where v represents the target drone's moving speed in m / s, freq is the center frequency in Hz, and c is the speed of light. From the above expression, it can be concluded that if the drone's moving speed is 20 m / s and the center frequency is 5.8 GHz, the single frequency deviation is approximately (20*5800*10^6) / (3*10^8)=386.67 Hz, and the double deviation is 773.34 Hz.

[0044] Normally, a PRACH with a 1.25 kHz subcarrier spacing supports a frequency deviation range of 625 Hz. However, according to the above estimation, when the operating frequency is high, a moving speed of only 20 m / s may cause the frequency deviation to exceed the tolerance of the channel estimation, which may lead to a loss of synchronization of the communication link in certain situations, such as the remote controller restarting or the aircraft being shot down by a jammer. This may make it impossible for the aircraft and the remote controller to reconnect and restore communication.

[0045] In an optional implementation 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 based on the correspondence. This can quickly determine the current signal scenario and provide a basis for determining the optimal target frequency hopping strategy.

[0046] Step 130: determine a target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back a target frequency point that matches the target frequency hopping strategy to the target UAV.

[0047] Optionally, in this embodiment, after determining the current signal scenario of the target UAV, a target frequency hopping strategy that matches the target UAV can be further determined based on the current signal scenario. Furthermore, a target frequency point can be determined based on the target frequency hopping strategy, and the target frequency point can be fed back to the target UAV so that the target UAV adjusts the operating frequency point to the target frequency point.

[0048] Optionally, in this embodiment, determining the target frequency hopping strategy of the target UAV that matches the current signal scenario may include: if the current signal scenario is determined to be a multipath effect, determining the target frequency hopping strategy to be random, cross-band frequency hopping; or, if the current signal scenario is determined to be dynamic interference, determining the first frequency point with the lowest comprehensive noise level at the central node, and performing frequency hopping based on the first frequency point; or, if the current signal scenario is determined to be signal fading, shifting the frequency band of the target UAV to a second frequency band; the frequency of the second frequency band is lower than the current frequency band.

[0049] In an optional implementation of this embodiment, if the current signal scenario is determined to be a multipath effect, then the target frequency hopping strategy can be determined as random, cross-band frequency hopping; in the specific implementation, by building different signal models, it can be found that there are large differences in the locations where fading occurs between different frequencies; under normal circumstances, frequency redundancy can be used to give priority to the use of wide-interval, cross-band frequency hopping to improve the signal fluctuations caused by the multipath effect.

[0050] In a specific example of this embodiment, when it is determined that the current signal scenario is a multipath effect, the current frequency point 24490 may be adjusted to the frequency point 14440, wherein the frequency point 14440 is the target frequency point involved in this embodiment.

[0051] In another optional implementation of this embodiment, for the scenario of dynamic random interference, the purpose of frequency hopping is to find a frequency point with a relatively stable channel environment, and determine the first frequency point with the lowest comprehensive noise level at the central node; illustratively, the accumulation of the master node noise and the slave node noise values ​​can be directly used to select a relatively small value as the stop hopping frequency point.

[0052] In another optional implementation of this embodiment, frequency selection is performed for high-speed moving scenarios. It can be seen from the single Doppler shift calculation formula that, in a scenario where the speed remains unchanged, the lower the working center frequency, the lower the Doppler frequency deviation. Therefore, in a scenario where it is found that the frequency hopping is caused by excessive frequency offset, the frequency hopping selection should be fixed across the frequency band to a lower frequency direction.

[0053] The technical solution of this embodiment is to obtain the measurement data of each relevant node associated with the target UAV at a set time interval during the operation of the target UAV through the central node in the star network; 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; 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 scene, and feed back the target frequency matching the target frequency hopping strategy to the target UAV, which solves the problem that the traditional frequency hopping strategy in the star network cannot adapt to the UAV environment, resulting in image transmission jamming, and can quickly and accurately determine the target frequency matching the current flight environment of the UAV, thereby improving the operation stability of the UAV.

[0054] Embodiment 2

[0055] Figure 2 1 is a flow chart of a method for determining a frequency hopping strategy of a drone according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with each optional solution in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0056] Step 210: During the operation of the target UAV, measurement data of each relevant node associated with the target UAV is obtained at set time intervals.

[0057] Step 220: Analyze each of the measurement data to obtain each analysis result.

[0058] Optionally, in this embodiment, analyzing each measurement data to obtain each analysis result may include: determining the fluctuation of the RSSI and the path loss to obtain a first analysis result; or, determining the CQI feedback of the target UAV, the change of the noise value, the noise change of the central node, and the fluctuation of the signal-to-noise ratio SNR to obtain a second analysis result; or, determining the frequency deviation of the target UAV based on the moving speed of the target UAV and the center frequency to obtain a third analysis result.

[0059] In an optional implementation 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 may be determined as the fluctuation of the RSSI and the path loss to obtain a first analysis result.

[0060] In another optional implementation of this embodiment, the difference between the CQI feedback, noise value change, noise change of the central node and signal-to-noise ratio SNR of the target UAV in the current period and the CQI feedback, noise value change, noise change of the central node and signal-to-noise ratio SNR of the target UAV in the previous period can be determined as the second analysis result.

[0061] In another optional implementation of this embodiment, the obtained moving speed and center frequency of the target drone can be substituted into In the formula, the frequency deviation of the target UAV is obtained, that is, the third analysis result involved in this embodiment; wherein v represents the moving speed of the target UAV, the unit is m / s, freq is the center frequency, the unit is Hz, and c is the speed of light.

[0062] Step 230: Determine the current signal scene of the target UAV based on each of the analysis results.

[0063] Optionally, in this embodiment, determining the current signal scenario of the target UAV based on each of the analysis results may include: if it is determined according to the first analysis result that the fluctuations of the RSSI and the path loss are greater than or equal to a first set fluctuation threshold, then determining that the current signal scenario is a multipath effect; if it is determined according to the second analysis result that the CQI feedback of the target UAV, the change in the noise value, the noise change of the central node, or the fluctuation of the SNR is greater than or equal to a second set fluctuation threshold, then determining that the current signal scenario is dynamic interference; if it is determined according to the third analysis result that the frequency deviation of the target UAV is greater than or equal to a third set threshold, then determining that the current signal scenario is signal fading.

[0064] In this embodiment, the specific values ​​of the first set fluctuation threshold, the second set fluctuation threshold and the third set threshold are not specifically limited.

[0065] In an optional implementation of this embodiment, if it is determined that the difference between the RSSI and path loss of the current period and the RSSI and path loss of the previous period is greater than or equal to the first set fluctuation threshold, it can be determined that the current signal scenario is a multipath effect; if it is determined that the CQI feedback, noise value change, noise change of the central node, and signal-to-noise ratio SNR of the target UAV in the current period and the CQI feedback, noise value change, noise change of the central node, and signal-to-noise ratio SNR of the target UAV in the previous period are greater than or equal to the second set fluctuation threshold, it is determined that the current signal scenario is dynamic interference; if the obtained moving speed and center frequency of the target UAV are substituted into In the formula, if the obtained frequency deviation of the target UAV is greater than or equal to the third set threshold, it is determined that the current signal scenario is signal fading.

[0066] Step 240: determine a target frequency hopping strategy of the target UAV that matches the current signal scenario, and feed back a target frequency point that matches the target frequency hopping strategy to the target UAV.

[0067] Step 250: If the current signal scenario includes multipath effect, dynamic interference and signal fading at the same time, process the multipath effect, dynamic interference and signal fading in sequence.

[0068] Optionally, in this embodiment, if it is determined that the current signal scenario simultaneously includes multipath effect, dynamic interference and signal fading, then the multipath effect, dynamic interference and signal fading can be processed in sequence, 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.

[0069] In order to better understand the method for determining the frequency hopping strategy of the drone involved in this embodiment, Figure 3 is a flow chart of another method for determining a frequency hopping strategy of a drone provided in Embodiment 2 of the present invention. Figure 3 The master node involved is the central node in the star network involved in this embodiment; the solution of this embodiment can select different stop-and-jump frequency selection strategies by monitoring different parameter changes within the cycle, thereby ensuring the stability of image transmission and data transmission of the drone flight.

[0070] The solution of this embodiment sets priorities for different signal scenarios when two or three signal scenarios of multipath effect, dynamic interference and signal fading occur at the same time, that is, the communication quality of the current link can be maintained and energy priority can be guaranteed; the image transmission and data stability of the drone flight can be guaranteed.

[0071] Embodiment 3

[0072] Figure 4 1 is a schematic diagram of the structure of a device for determining a frequency hopping strategy of a drone according to Embodiment 3 of the present invention. Figure 4 As shown, the device can be configured in a central node in a star network, and the device includes: a measurement data acquisition module 410, a current signal scenario determination module 420, and a target frequency hopping strategy determination module 430.

[0073] The measurement data acquisition module 410 is used to acquire the measurement data of each relevant node associated with the target UAV at each set time period during the operation of the target UAV;

[0074] The current signal scene determination module 420 is used to 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; the current signal scene includes at least one of the following: multipath effect, dynamic interference and signal fading;

[0075] The target frequency hopping strategy determination module 430 is used to 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.

[0076] The solution of this embodiment is to obtain the measurement data of each relevant node associated with the target UAV at set time intervals during the operation of the target UAV through the measurement data acquisition module; analyze each of the measurement data through the current signal scene determination module to obtain each analysis result, and determine the current signal scene of the target UAV based on each of the analysis results; determine the target frequency hopping strategy of the target UAV that matches the current signal scene through the target frequency hopping strategy determination module, and feed back the target frequency matching the target frequency hopping strategy to the target UAV, thereby solving the problem that the traditional frequency hopping strategy in the star network cannot adapt to the UAV environment, resulting in image transmission jamming, and can quickly and accurately determine the target frequency matching the current flight environment of the UAV, thereby improving the operation stability of the UAV.

[0077] In an optional implementation of this embodiment, the measurement data includes:

[0078] Received signal strength indication RSSI, path loss, periodic channel instruction indication CQI feedback of the target UAV, change in noise value, noise change and signal-to-noise ratio SNR of the central node, speed of the target UAV and center frequency.

[0079] In an optional implementation of this embodiment, the current signal scenario determination module 420 is specifically used to determine the fluctuation of the RSSI and the path loss to obtain a first analysis result;

[0080] or,

[0081] Determine the CQI feedback of the target UAV, the change of the noise value, the noise change of the central node, and the fluctuation of the signal-to-noise ratio SNR to obtain a second analysis result;

[0082] or,

[0083] The frequency deviation of the target UAV is determined based on the moving speed and the center frequency of the target UAV to obtain a third analysis result.

[0084] In an optional implementation of this embodiment, the current signal scenario determination module 420 is further specifically configured to determine that the current signal scenario is a multipath effect if it is determined according to the first analysis result that the fluctuation of the RSSI and the path loss is greater than or equal to a first set fluctuation threshold;

[0085] If it is determined according to the second analysis result that the CQI feedback of the target UAV, the change in the noise value, the noise change of the central node, or the fluctuation of the SNR is greater than or equal to a second set fluctuation threshold, then the current signal scenario is determined to be dynamic interference;

[0086] If it is determined according to the third analysis result that the frequency deviation of the target UAV is greater than or equal to a third set threshold, the current signal scenario is determined to be signal fading.

[0087] In an optional implementation of this embodiment, the target frequency hopping strategy determination module 430 is specifically configured to determine the target frequency hopping strategy as random, cross-band frequency hopping if it is determined that the current signal scenario is a multipath effect;

[0088] or,

[0089] If it is determined that the current signal scenario is dynamic interference, determining a first frequency point with the lowest comprehensive noise level at the central node, and performing a trip based on the first frequency point;

[0090] or,

[0091] If it is determined that the current signal scenario is signal fading, the frequency band of the target UAV is shifted to a second frequency band; the frequency of the second frequency band is lower than the current frequency band.

[0092] In an optional implementation of this embodiment, the device further includes: a priority determination module, which is used to process the multipath effect, dynamic interference and signal fading in sequence if the current signal scenario includes multipath effect, dynamic interference and signal fading at the same time.

[0093] The device for determining the frequency hopping strategy of a drone provided in an embodiment of the present invention can execute the method for determining the frequency hopping strategy of a drone provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0094] Embodiment 4

[0095] Figure 5A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent 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 processing, cellular phones, smart phones, 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 and are not intended to limit the implementation of the present invention described and / or required herein.

[0096] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0098] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a frequency hopping strategy of a drone, the method comprising: during the operation of the target drone, obtaining measurement data of each relevant node associated with the target drone at each set time period; analyzing each of the measurement data to obtain each analysis result, and determining the current signal scene of the target drone 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; determining a target frequency hopping strategy of the target drone that matches the current signal scene, and feeding back a target frequency point that matches the target frequency hopping strategy to the target drone.

[0099] In some embodiments, the method for determining the frequency hopping strategy of the drone may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the frequency hopping strategy of the drone described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the frequency hopping strategy of the drone in any other appropriate manner (e.g., by means of firmware).

[0100] Various implementations 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 products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] Computer programs for implementing the methods of the present invention 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0102] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0104] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0105] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0106] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0107] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0108] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the database detection method provided in any embodiment of the present application.

[0109] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0110] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0111] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining a frequency hopping strategy of an unmanned aerial vehicle, executed by a central node in a star network, characterized in that: The method comprises: During the operation of the target UAV, the measurement data of each relevant node associated with the target UAV is obtained at each set time period; 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; the current signal scene includes at least one of the following: multipath effect, dynamic interference and signal fading; A target frequency hopping strategy of the target UAV that matches the current signal scenario is determined, and a target frequency point that matches the target frequency hopping strategy is fed back to the target UAV.

2. The method for determining the frequency hopping strategy of a drone according to claim 1, characterized in that: The measurement data include: Received signal strength indication RSSI, path loss, periodic channel instruction indication CQI feedback of the target UAV, change in noise value, noise change and signal-to-noise ratio SNR of the central node, speed of the target UAV and center frequency.

3. The method for determining the frequency hopping strategy of a drone according to claim 2, characterized in that: The analyzing of each of the measurement data to obtain each analysis result includes: Determine the fluctuation of the RSSI and the path loss to obtain a first analysis result; or, Determine the CQI feedback of the target UAV, the change of the noise value, the noise change of the central node, and the fluctuation of the signal-to-noise ratio SNR to obtain a second analysis result; or, The frequency deviation of the target UAV is determined based on the moving speed and the center frequency of the target UAV to obtain a third analysis result.

4. The method for determining the frequency hopping strategy of a drone according to claim 3, characterized in that: The determining the current signal scene of the target UAV based on each of the analysis results includes: If it is determined according to the first analysis result that the fluctuations of the RSSI and the path loss are greater than or equal to a first set fluctuation threshold, it is determined that the current signal scenario is a multipath effect; If it is determined according to the second analysis result that the CQI feedback of the target UAV, the change in the noise value, the noise change of the central node, or the fluctuation of the SNR is greater than or equal to a second set fluctuation threshold, then the current signal scenario is determined to be dynamic interference; If it is determined according to the third analysis result that the frequency deviation of the target UAV is greater than or equal to a third set threshold, the current signal scenario is determined to be signal fading.

5. The method for determining the frequency hopping strategy of a drone according to claim 1, characterized in that: The determining of a target frequency hopping strategy of the target UAV matching the current signal scenario includes: If it is determined that the current signal scenario is a multipath effect, determining the target frequency hopping strategy to be random, cross-band frequency hopping; or, If it is determined that the current signal scenario is dynamic interference, determining a first frequency point with the lowest comprehensive noise level at the central node, and performing a trip based on the first frequency point; or, If it is determined that the current signal scenario is signal fading, the frequency band of the target UAV is shifted to a second frequency band; the frequency of the second frequency band is lower than the current frequency band.

6. The method for determining the frequency hopping strategy of a drone according to claim 1, characterized in that: The method further comprises: If the current signal scenario includes multipath effect, dynamic interference and signal fading at the same time, the multipath effect, dynamic interference and signal fading are processed in sequence.

7. A device for determining a frequency hopping strategy of an unmanned aerial vehicle, configured in a central node in a star network, characterized in that: The device comprises: A measurement data acquisition module, used to acquire measurement data of each relevant node associated with the target UAV at set time intervals during the operation of the target UAV; A current signal scene determination module, used to 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; 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 used to 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.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the frequency hopping strategy of the drone according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a frequency hopping strategy of a drone according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining a frequency hopping strategy of a drone according to any one of claims 1 to 6 is implemented.

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