A method for eliminating dynamic data of unmanned aerial vehicle, storage medium and electronic device

Abstract

The present invention relates to the field of data processing, and in particular to a method for eliminating dynamic data of unmanned aerial vehicles, a storage medium and an electronic device. The method comprises: matching the tail dynamic data sequence in the target dynamic data sequence with the determination data sequence in the determination database corresponding to the target dynamic data sequence, and generating a second update waiting time of the target dynamic data sequence; if the target dynamic data sequence exceeds the second update waiting time and still does not receive new status update information, then eliminating the target dynamic data sequence. By matching the tail dynamic data sequence with the landing dynamic data sequence in other data sequences of completed flight missions in the same flight area, it is roughly determined in which interval of the landing dynamic data sequence the tail dynamic data sequence is roughly located, and then the remaining flight time of the corresponding unmanned aerial vehicle can be determined, thereby more accurately removing the dynamic data sequence of the completed flight mission in the buffer.

Description

Background Art

[0002] During the flight of the drone, in order to timely monitor and assist the drone's flight mission, the drone needs to send data back to the ground station in real time. The data sent back includes but is not limited to flight altitude, speed, attitude, location, battery power, etc. This helps ensure the safety of the drone's flight and promptly detect and respond to potential problems, such as low battery, deviation from the route, or mechanical failure.

[0003] The UAV ground station is an important part of the UAV system. It acts as the command and control center of the UAV, responsible for monitoring, managing and coordinating the flight activities of the UAV. The ground station integrates a series of hardware and software facilities to ensure that the operator can effectively interact with the UAV and achieve comprehensive supervision and assistance of the UAV even at a long distance.

[0004] In order to monitor and assist the currently flying unmanned vehicles more promptly and quickly, the real-time status data sent back by all flying drones are usually stored in a buffer for high-speed call processing. In the prior art, the corresponding status data sequence is usually removed from the buffer only after receiving the end of the flight mission of the drone. However, in the actual flight process, the ground station is often unable to receive some of the real-time status data sent back by the drone due to terrain obstruction or network problems. This will result in some data sequences being stored in the buffer and occupying memory because they cannot receive the end of the mission information. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is:

[0006] According to one aspect of the present invention, a method for eliminating dynamic data of a drone is provided, the method comprising the following steps:

[0007] If the dynamic data sequence corresponding to any drone in the cache does not receive new status update information for more than the first update waiting time, the dynamic data sequence is determined as the target dynamic data sequence; the status update information includes drone ID, longitude, latitude, flight altitude and flight speed information;

[0008] Matching the tail dynamic data sequence in the target dynamic data sequence with the determination data sequence in the determination database corresponding to the target dynamic data sequence; the sequence length of the tail dynamic data sequence is less than the length of the determination data sequence;

[0009] A second update waiting time T of the target dynamic data sequence is generated according to the highest matching segment in the determination data sequence with the highest matching degree with the tail dynamic data sequence; the second update waiting time T is greater than the first update waiting time T satisfies the following conditions:

[0010]

[0011] in, The sequence tail time of the judgment data sequence with the highest matching degree with the tail dynamic data sequence; The segment tail time of the highest matching segment in the determination data sequence with the highest matching degree with the tail dynamic data sequence;

[0012] If the target dynamic data sequence still does not receive new status update information after the second update waiting time, the target dynamic data sequence is deleted;

[0013] The determination data sequence is generated according to the following steps:

[0014] Acquire multiple complete historical dynamic data sequences in the target flight area;

[0015] Using a portion of the dynamic data sequence in each historical dynamic data sequence that is in the preset landing period as a landing dynamic data sequence;

[0016] Perform clustering processing on multiple landing dynamic data sequences to generate multiple landing data groups;

[0017] Generate a group landing representative data sequence corresponding to each landing data group according to the dynamic data sequence in each landing data group;

[0018] The representative data sequence of the group is used as the judgment data sequence.

[0019] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for eliminating dynamic data of a drone is implemented.

[0020] According to a third aspect of the present invention, there is provided an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for eliminating dynamic data of a drone when executing the computer program.

[0021] The present invention has at least the following beneficial effects:

[0022] In the present invention, by matching the tail dynamic data sequence in the dynamic data sequence (i.e., the target dynamic data sequence) in which data update is currently suspended with the landing dynamic data sequence in other data sequences that have completed flight missions in the same flight area, it is roughly determined in which interval of the landing dynamic data sequence the tail dynamic data sequence is roughly located, and then the remaining flight time of the corresponding unmanned aerial vehicle, i.e., T, can be determined. Then T is used as the waiting time for the current dynamic data sequence to wait for update. If the corresponding target dynamic data sequence has not received new status update information after the update waiting time exceeds the second update waiting time, the target dynamic data sequence is eliminated. In this way, the dynamic data sequence corresponding to the unmanned aerial vehicle that has completed the flight mission in the buffer can be removed more accurately and timely, so that there is more free space in the buffer. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 A flow chart of a method for eliminating dynamic data of a drone provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] As a possible embodiment of the present invention, Figure 1 As shown, a method for eliminating dynamic data of a drone is provided, and the method comprises the following steps:

[0027] S100: If the dynamic data sequence corresponding to any drone in the cache does not receive new status update information for more than the first update waiting time, the dynamic data sequence is determined as the target dynamic data sequence. The status update information includes drone ID, longitude, latitude, flight altitude and flight speed information.

[0028] The frequency of data transmission from drones to ground stations is not fixed. It depends on multiple factors, and different application scenarios have different requirements for data real-time performance. For example, monitoring or mapping tasks may require high-frequency data transmission to achieve real-time monitoring or high-definition image construction, while scientific research and exploration tasks may require lower data update frequency.

[0029] Therefore, the UAV system will set a reasonable data return interval according to the specific usage scenario, which may be a few milliseconds (for scenarios that require low-latency control) to a few minutes (for transmission with large data volumes and low real-time requirements). For example, in some professional applications, the data return interval can be set to once every few seconds to tens of seconds, while in applications that require continuous video streaming, it may be almost real-time. Therefore, the specific return time needs to be determined based on the design and mission requirements of the specific UAV system.

[0030] Therefore, the corresponding first update waiting time will also be determined according to the data return frequency of the specific drone. For example, the first update waiting time may be the length of the time period corresponding to one or more data update cycles.

[0031] During the flight of the drone, one or a few consecutive update data may fail to be transmitted to the ground station. In this case, the dynamic data sequence will receive the status update data again after a short waiting time. If the update data cannot be transmitted to the ground station during the landing of the drone, the dynamic data sequence will be unable to receive the mission end information. At this time, the dynamic data sequence cannot receive the status update data again in the subsequent time.

[0032] Therefore, by setting the first update waiting time, the probability of the dynamic data erasing method in the present invention being falsely started can be prevented by limiting the waiting time, thereby reducing the probability of falsely starting the data clearing method in this embodiment.

[0033] S200: Matching the tail dynamic data sequence in the target dynamic data sequence with the determination data sequence in the determination database corresponding to the target dynamic data sequence. The length of the tail dynamic data sequence is less than the length of the determination data sequence.

[0034] The determination data sequence is generated according to the following steps:

[0035] S201: Acquire multiple complete historical dynamic data sequences in the target flight area. The complete historical dynamic data sequence is a dynamic data sequence including take-off marks and landing marks.

[0036] Usually, the flight environment corresponding to the drones in a flight area is basically the same, so the flight characteristics shown during the flight are basically the same, such as the overlap of routes, flight time, power consumption and other characteristics. In addition, for drones in the same usage scenario, the design overlap of their routes is higher. Usually, at least one flight route can be planned according to different usage scenarios and different mission requirements. Therefore, historical data can be compared and matched with the status data in the current flight mission to predict the current flight status of the drone.

[0037] S202: taking a portion of the dynamic data sequence in each historical dynamic data sequence that is in a preset landing period as a landing dynamic data sequence. The preset landing period may be a period from 10 minutes to 30 minutes before the landing mark.

[0038] After screening in S100 in this embodiment, the tail dynamic data sequence in the target dynamic data sequence is usually data generated by the drone during landing, or flight data generated during the preparation for landing. Therefore, in this step, some dynamic data sequences in the preset landing period will also be selected for subsequent matching processing.

[0039] S203: performing clustering processing on the multiple landing dynamic data sequences to generate multiple landing data groups.

[0040] Since the data sequence in this embodiment can be regarded as a vector, each element of which includes information such as longitude, latitude, flight altitude and flight speed.

[0041] The clustering method used in this step may be K-means clustering method, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), mean shift clustering method, etc. Among them, the K value of the K-means clustering method can be set according to the types of flight routes existing in the actual use scenario.

[0042] S204: Generate a group landing representative data sequence corresponding to each landing data group according to the dynamic data sequence in each landing data group.

[0043] There are several common methods for generating representative vectors for each group (or cluster) after clustering, depending on the clustering algorithm used and the requirements of the actual application. For example, for algorithms such as K-means, the representative vector of each cluster (that is, the group landing representative data sequence or the group navigation representative data sequence) is usually the average coordinate of all points in the cluster, and this average vector is the centroid. The calculation method is to take the mean of all samples in the cluster in each dimension.

[0044] S205: The group representative data sequence is used as the determination data sequence.

[0045] After S204, the method further includes:

[0046] S214: The partial dynamic data sequence in each historical dynamic data sequence that is in the preset flight period is used as the flight dynamic data sequence. The length of the flight dynamic data sequence is less than or equal to the length of the landing dynamic data sequence. The preset flight period in this embodiment can be the period before the preset landing period, such as 5 minutes before the preset landing period.

[0047] S224: Clustering the multiple navigation dynamic data sequences to generate multiple navigation data groups.

[0048] S234: Generate a group navigation representative data sequence corresponding to each navigation data group according to the dynamic data sequence in each navigation data group.

[0049] S244: Matching the landing representative data sequence of each group with the navigation representative data sequence of each group.

[0050] The matching process of the group landing representative data sequence and the group navigation representative data sequence is specifically to align the head of the group navigation representative data sequence with the head of the group landing representative data sequence, and then slide the group navigation representative data sequence to the tail of the group landing representative data sequence in sequence, and calculate the vector similarity of the two sequences of the overlapping parts after each sliding.

[0051] The operations from S214 to S244 are similar to those from S202 to S204, except that the processed data segments are different, so they will not be described in detail.

[0052] S254: Generate a determination data sequence based on the matching result.

[0053] Since the above landing dynamic data sequence is selected by a preset landing period defined by humans, in order to fully include the flight characteristics during the landing process, the length of the preset landing period will be increased, and it is inevitable that a part of the data sequence of the normal navigation of the drone will be included in the landing dynamic data sequence. Therefore, in order to remove the data sequence of the normal navigation part included in the final group landing representative data sequence, it is necessary to match each group landing representative data sequence with each group navigation representative data sequence to determine the position of the normal navigation data sequence included in the group landing representative data sequence and remove it.

[0054] Among them, S254 includes the following situations:

[0055] S2541: If the matching degree between any group navigation representative data sequence and the target subsequence in the group landing representative data sequence is greater than the first matching threshold, and the length of the sequence after the target subsequence in the group landing representative data sequence is greater than or equal to the length of the tail dynamic data sequence, then the sequence after the target subsequence in the group landing representative data sequence is used as the determination data sequence, and the length of the target subsequence is the same as the length of the group navigation representative data sequence. The first matching threshold may be 0.8.

[0056] S2541 can remove the normal navigation data sequence contained in the landing dynamic data sequence, so that the remaining data sequence is basically a purer characteristic sequence of the landing process.

[0057] S2542: If the matching degree between any group navigation representative data sequence and the target subsequence in the group landing representative data sequence is greater than a first matching threshold, and the length of the sequence after the target subsequence in the group landing representative data sequence is less than a length threshold, the group landing representative data sequence is removed.

[0058] The length threshold in S2542 can generally be the length of a few and rare landing feature sequences. Usually, the length of the rare sequences is relatively short. It may be a data sequence corresponding to the situation where most of the signals cannot be transmitted to the ground station during the landing process, or it may be a data sequence corresponding to the emergency landing caused by other reasons. The above cases are all rare individual cases, so they are generally not considered and need to be removed.

[0059] Usually, the length of a group with only a few members after clustering or a few individual sequences outside the group can be selected as the length threshold.

[0060] S2543: If the matching degree between any group navigation representative data sequence and all target subsequences in the group landing representative data sequence is less than the first matching threshold, the group landing representative data sequence is used as the determination data sequence.

[0061] S300: Generate a second update waiting time T of the target dynamic data sequence according to the highest matching segment in the determination data sequence with the highest matching degree with the tail dynamic data sequence. The second update waiting time is greater than the first update waiting time. T satisfies the following conditions:

[0062]

[0063] in, It is the sequence tail time of the judgment data sequence that has the highest matching degree with the tail dynamic data sequence. It is the segment tail time of the highest matching segment in the determination data sequence that has the highest matching degree with the tail dynamic data sequence.

[0064] The matching process in this step is the same as that in S244 above, and will not be elaborated here.

[0065] In this embodiment, by matching the tail dynamic data sequence in the dynamic data sequence where the current data update is paused (i.e., the target dynamic data sequence) with the landing dynamic data sequence in the data sequences of other completed flight tasks in the same flight area, it is roughly determined which interval in the landing dynamic data sequence the tail dynamic data sequence is roughly located in, and then the remaining flight time of the corresponding unmanned aerial vehicle, that is, T, can be determined. Then, T is used as the waiting duration for the current dynamic data sequence to wait for update. If the update waiting duration of the corresponding target dynamic data sequence exceeds the second update waiting duration and no new status update information is received yet, the target dynamic data sequence is eliminated. Thus, the dynamic data sequences corresponding to the unmanned aerial vehicles that have completed the flight tasks in the buffer can be removed more accurately and timely, so that there is more free space in the buffer.

[0066] S400: If the target dynamic data sequence still does not receive new status update information after exceeding the second update waiting duration, the target dynamic data sequence is eliminated.

[0067] The status update information also includes the remaining battery power.

[0068] After generating the second update waiting duration T of the target dynamic data sequence in S300, the method further includes:

[0069] S500: Generate the remaining flight time T of the unmanned aerial vehicle according to the remaining battery power s 。

[0070] S600: If T s < T, then when the target dynamic data sequence still does not receive new status update information after exceeding T s The target dynamic data sequence is eliminated in this step. Eliminating the target dynamic data sequence means deleting the target dynamic data sequence from the buffer.

[0071] Since the information on the remaining battery power of the unmanned aerial vehicle can also be received in the status update information, the remaining flight duration of the unmanned aerial vehicle can be predicted through the remaining battery power. Furthermore, two dimensions of the remaining flight duration and the second update waiting duration can be used together to determine whether to eliminate the target dynamic data sequence, so as to improve the accuracy of data elimination.

[0072] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0073] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0074] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0075] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0076] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0077] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0078] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0079] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0080] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0081] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0082] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0083] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0084] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0085] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 above.

[0086] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0087] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0088] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0089] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0090] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0091] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for eliminating dynamic data of unmanned aerial vehicles, characterized in that: The method comprises the following steps: If no new status update information is received in the dynamic data sequence corresponding to any UAV in the cache for more than the first update waiting time, the dynamic data sequence is determined as the target dynamic data sequence; the status update information includes the UAV ID, longitude, latitude, flight altitude and flight speed information; Matching a tail dynamic data sequence in the target dynamic data sequence with a determination data sequence in a determination database corresponding to the target dynamic data sequence; the sequence length of the tail dynamic data sequence is less than the length of the determination data sequence; A second update waiting time T of the target dynamic data sequence is generated according to the highest matching segment in the determination data sequence with the highest matching degree with the tail dynamic data sequence; the second update waiting time T is greater than the first update waiting time T; and T satisfies the following conditions: in, The sequence tail time of the judgment data sequence with the highest matching degree with the tail dynamic data sequence; The segment tail time of the highest matching segment in the determination data sequence with the highest matching degree with the tail dynamic data sequence; If the target dynamic data sequence still does not receive new status update information after the second update waiting time, the target dynamic data sequence is deleted; The determination data sequence is generated according to the following steps: Acquire multiple complete historical dynamic data sequences in the target flight area; Using a portion of the dynamic data sequence in each historical dynamic data sequence that is in the preset landing period as a landing dynamic data sequence; Perform clustering processing on multiple landing dynamic data sequences to generate multiple landing data groups; Generate a group landing representative data sequence corresponding to each landing data group according to the dynamic data sequence in each landing data group; The representative data sequence of the group is used as the determination data sequence.

2. The method according to claim 1, characterized in that After generating a group landing representative data sequence corresponding to each landing data group, the method further includes: A part of the dynamic data sequence in each historical dynamic data sequence that is located in a preset flight period is used as a flight dynamic data sequence; the length of the flight dynamic data sequence is less than or equal to the length of the landing dynamic data sequence; Perform clustering processing on multiple navigation dynamic data sequences to generate multiple navigation data groups; Generate a group navigation representative data sequence corresponding to each navigation data group according to the dynamic data sequence in each navigation data group; Matching the representative landing data sequence of each group with the representative navigation data sequence of each group; According to the matching results, a judgment data sequence is generated.

3. The method according to claim 2, characterized in that According to the matching results, a judgment data sequence is generated, including: If the matching degree between any group navigation representative data sequence and the target subsequence in the group landing representative data sequence is greater than the first matching threshold, and the length of the sequence after the target subsequence in the group landing representative data sequence is greater than or equal to the sequence length of the tail dynamic data sequence, then the sequence after the target subsequence in the group landing representative data sequence is used as the judgment data sequence, and the length of the target subsequence is the same as the length of the group navigation representative data sequence.

4. The method according to claim 3, characterized in that According to the matching results, a judgment data sequence is generated, including: If the matching degree between any group navigation representative data sequence and the target subsequence in the group landing representative data sequence is greater than the first matching threshold, and the length of the sequence after the target subsequence in the group landing representative data sequence is less than the length threshold, the group landing representative data sequence is removed.

5. The method according to claim 3, characterized in that: According to the matching results, a judgment data sequence is generated, including: If the matching degree between any group navigation representative data sequence and all target subsequences in the group landing representative data sequence is less than the first matching threshold, the group landing representative data sequence is used as the determination data sequence.

6. The method according to claim 1, characterized in that The status update information also includes the remaining power; After generating a second update waiting time T of the target dynamic data sequence, the method further includes: Generate the remaining flight time T of the drone based on the remaining power s ; If T s < T, then when the target dynamic data sequence exceeds T s and still no new status update information is received, the target dynamic data sequence will be eliminated.

7. The method according to claim 1, characterized in that The complete historical dynamic data sequence is a dynamic data sequence including take-off marks and landing marks.

8. The method according to claim 7, characterized in that The preset landing period is the period from 10 minutes to 30 minutes before the landing mark.

9. A non-transitory computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for eliminating dynamic data of a drone as described in any one of claims 1 to 8 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for eliminating dynamic data of a drone as described in any one of claims 1 to 8 is implemented.

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