Unmanned aerial vehicle cluster anomaly monitoring method and device, electronic equipment and readable storage medium

Abstract

The invention provides an unmanned aerial vehicle cluster abnormity monitoring method and device, electronic equipment and a readable storage medium, and the method comprises the steps: receiving broadcast information which is broadcasted by each unmanned aerial vehicle in an unmanned aerial vehicle cluster according to the sequence in a time-sharing manner, and calculating the measurement position and the measurement speed of each unmanned aerial vehicle; wherein the broadcast information comprises position information and speed information of each unmanned aerial vehicle; and determining an abnormal unmanned aerial vehicle in the unmanned aerial vehicle cluster based on the broadcast information, the measurement position and the measurement speed, and performing anomaly processing on the abnormal unmanned aerial vehicle. According to the method, the abnormal unmanned aerial vehicle in the unmanned aerial vehicle cluster can be rapidly and accurately detected, and more damage is avoided by performing exception processing on the monitored abnormal unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of drone cluster technology, and in particular to a drone cluster anomaly monitoring method, device, electronic device and readable storage medium. Background Art

[0002] With the continuous advancement and maturity of drone technology, drone swarming technology has gradually demonstrated its potential for widespread application in various fields. By coordinating and controlling multiple drones to achieve collaborative operations, this technology can not only significantly improve mission execution efficiency but also enhance the robustness and flexibility of the system.

[0003] Specifically, in the military, drone swarms are used to perform critical missions such as reconnaissance, strike, and communications relay. In the civilian sector, drone swarms are used for environmental monitoring, disaster relief, and traffic regulation. In the commercial sector, drone swarms are used in a variety of scenarios, including logistics and distribution, agricultural monitoring, power inspections, geographic mapping, and film and television production. With its unique advantage of multi-machine collaboration, drone swarm technology is playing a vital role in numerous applications and demonstrates a broad market potential.

[0004] However, the application of drone swarm technology is not without its challenges. Compared to operating a single drone, the management complexity of a drone swarm is significantly increased. This complexity is particularly pronounced when a drone in the swarm experiences a malfunction or anomaly. An anomaly in a single drone can quickly spread to the entire swarm, causing widespread functional failure or equipment damage. Furthermore, in many cases, drones in abnormal conditions struggle to identify their own problems. This requires a mechanism to promptly detect these anomalies and take effective action to address them.

[0005] Therefore, with the development of drone swarm technology, how to quickly and accurately identify abnormal drones in the swarm and implement effective response strategies has become a key technical link that needs to be solved urgently. Summary of the Invention

[0006] The present invention provides a drone cluster anomaly monitoring method, device, electronic device and readable storage medium, which are used to overcome the defects of existing drone clusters that are unable to quickly and accurately identify abnormal drones and take measures against abnormal drones, and realize the rapid identification and processing of abnormal individuals in the drone cluster.

[0007] On the one hand, the present invention provides a method for monitoring abnormalities in a drone cluster, comprising: receiving broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculating the measured position and measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; based on the broadcast information, the measured position and the measured speed, determining the abnormal drone in the drone cluster, and performing abnormal processing on the abnormal drone.

[0008] Furthermore, the step of calculating the measurement position and measurement speed of each drone specifically includes: determining the number of drones in the drone cluster; when the number of drones is greater than or equal to a set number threshold, using the arrival time difference method to calculate the measurement position of each drone, and using the Doppler speed measurement algorithm to calculate the measurement speed of each drone; when the number of drones is less than the set number threshold, using the arrival difference lateral algorithm to calculate the measurement position of each drone, and using the Doppler speed measurement algorithm to calculate the measurement speed of each drone.

[0009] Furthermore, the determining of abnormal drones in the drone cluster based on the broadcast information, the measured position and the measured speed includes: comparing the position information in the broadcast information with the measured position to obtain a position difference; comparing the speed information in the broadcast information with the measured speed to obtain a speed difference; when the position difference is higher than a first set difference, and / or the speed difference is higher than a second set difference, determining the drone corresponding to the broadcast information as the abnormal drone.

[0010] Furthermore, the step of performing abnormal processing on the abnormal drone specifically includes: when the power of the abnormal drone is normal, taking over the motor control of the abnormal drone through additional allocated computing resources, and no longer using the sensor information of the abnormal drone; when the power of the abnormal drone is abnormal, or the abnormal drone is still abnormal after being taken over, abandoning the abnormal drone and determining the movement path of the abnormal drone to control other drones in the drone cluster to leave the area where the movement path of the abnormal drone is located.

[0011] Furthermore, each drone in the drone cluster has a self-monitoring function, and the broadcast information also includes a self-test result; accordingly, the broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster is received, which includes: obtaining the drone's own power data and sensor data; judging whether the drone itself has an abnormality based on the power data and sensor data, and obtaining the self-test result in the broadcast information; wherein, the self-test result in the broadcast information includes a normal status or an abnormal status.

[0012] Furthermore, when the self-test result is an abnormal state, the drone is subjected to abnormal processing according to the self-test result; when the self-test result is a normal state, the broadcast information is received and the corresponding measurement position and measurement speed are calculated; based on the broadcast information, the measurement position and the measurement speed, the abnormal drone in the drone cluster is determined, and the abnormal drone is subjected to abnormal processing.

[0013] In the second aspect, the present invention also provides a drone cluster anomaly monitoring device, including: an information receiving and calculation module, used to receive broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculate the measurement position and measurement speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; an anomaly monitoring and processing module, used to determine the abnormal drone in the drone cluster based on the broadcast information, the measurement position and the measurement speed, and perform anomaly processing on the abnormal drone.

[0014] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements any of the above-described methods for monitoring abnormalities in a drone cluster.

[0015] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described drone cluster anomaly monitoring methods.

[0016] In a fifth aspect, the present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described drone cluster anomaly monitoring methods.

[0017] The drone swarm anomaly monitoring method provided by the present invention receives broadcast information from each drone in the swarm in a sequential, time-sharing manner and calculates the measured position and speed of each drone. The broadcast information includes the position and speed of each drone. Based on the broadcast information, the measured position and speed, the method identifies abnormal drones in the swarm and performs abnormal processing on them. This method can quickly and accurately detect abnormal drones in a swarm and, by performing abnormal processing on detected abnormal drones, avoids further damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of the process of monitoring abnormalities of drone clusters provided by an embodiment of the present invention.

[0020] Figure 2 This is one of the schematic diagrams for calculating the measurement position of a drone provided in an embodiment of the present invention.

[0021] Figure 3 4 is a schematic diagram of calculating the measured speed of a drone provided by an embodiment of the present invention.

[0022] Figure 4 This is the second schematic diagram of calculating the measurement position of the drone provided in an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the overall process of the drone cluster anomaly monitoring method provided by an embodiment of the present invention.

[0024] Figure 6 It is a structural diagram of the drone cluster anomaly monitoring device provided by an embodiment of the present invention.

[0025] Figure 7 It is a schematic diagram of the entity structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0027] Figure 1 FIG. 1 is a flow chart showing a method for monitoring abnormalities in a drone cluster according to an embodiment of the present invention. Figure 1 As shown, the method includes: S110, receiving broadcast information broadcasted sequentially and time-sharingly by each drone in a drone cluster, and calculating the measured position and measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; S120, based on the broadcast information, the measured position and the measured speed, determining abnormal drones in the drone cluster, and performing abnormal processing on the abnormal drones.

[0028] The following will describe steps S110 - S120 and related steps in detail.

[0029] S110, receiving broadcast information broadcasted sequentially and time-divided by each drone in the drone cluster, and calculating a measured position and a measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone.

[0030] It is easy to understand that a drone swarm includes drones that play different roles, such as master drones, monitoring drones and ordinary drones, and different types of drones undertake different tasks.

[0031] Specifically, the master drone is responsible for mission planning, route planning, and dynamic adjustments for the entire drone swarm, ensuring that all drones can efficiently and harmoniously complete their assigned tasks. Monitoring drones monitor the status of other drones in the swarm, including flight parameters and health status, promptly identifying potential issues and reporting them to the master drone. Standard drones are responsible for executing specific tasks, such as data collection and material transportation, acting according to the master drone's instructions.

[0032] The number of master drones, monitoring drones, and regular drones in a drone swarm can be determined based on practical needs and is not specifically limited here. Furthermore, the number of master drones, monitoring drones, and regular drones can be dynamically altered during actual use. For example, if all monitoring drones experience an abnormality, a functioning regular drone can be selected to replace the abnormal monitoring drone and complete the corresponding task.

[0033] In this embodiment, each drone in the drone cluster will broadcast its position information and speed information to the surrounding area in a sequential time-sharing manner, which is collectively referred to as broadcast information. The position information here includes geographic coordinates and relative position, and the speed information includes speed and direction.

[0034] It should be noted that in addition to location information and speed information, broadcast information can also include a lot of other relevant information, such as battery status, flight mode, sensor data, environmental perception data, task progress, communication link quality, load status, etc., which are not specifically limited here.

[0035] This embodiment uses a sequential time-sharing method to broadcast information, which can ensure that each drone broadcasts information in an orderly and time-sharing manner, thereby avoiding channel conflicts caused by simultaneous broadcasting of information and improving overall communication efficiency.

[0036] The monitoring drones in the drone cluster will receive the broadcast information transmitted by other drones in real time. At the same time, the monitoring drones will also use radio signals to calculate the measurement position and measurement speed of other drones for subsequent use in determining whether each drone is abnormal.

[0037] Based on receiving the broadcast information broadcasted sequentially and time-divided by each drone in the drone cluster in step S110 and calculating the measured position and measured speed of each drone, step S120 is further executed.

[0038] S120: Determine abnormal drones in the drone cluster based on the broadcast information, the measurement position, and the measurement speed, and perform abnormal processing on the abnormal drones.

[0039] It's easy to understand that the position and speed information of each drone in the broadcast information is compared with the corresponding measured position and speed. If the difference between the comparisons is higher than the set threshold, it means that the corresponding drone has experienced an abnormality and will be identified as an abnormal drone. Conversely, if the difference between the comparisons is lower than the set threshold, the corresponding drone is in a normal operating state.

[0040] After identifying the abnormal drone, the master drone will perform exception processing for the abnormal drone. The exception processing here includes but is not limited to: allocating additional computing resources to regain control of the abnormal drone, and / or abandoning the abnormal drone, determining the movement path of the abnormal drone, and controlling other drones to leave the relevant area of ​​the movement path to avoid unnecessary collisions.

[0041] In this embodiment, the system receives broadcast information from each drone in a swarm, broadcasted sequentially and time-sharingly, and calculates the measured position and speed of each drone. The broadcast information includes the position and speed of each drone. Based on the broadcast information, the measured position and speed, the system identifies abnormal drones in the swarm and performs exception handling on them. This method can quickly and accurately detect abnormal drones in a swarm, and by performing exception handling on detected abnormal drones, further damage can be avoided.

[0042] On the basis of the above embodiment, the calculation process of the measurement speed and measurement position of each UAV will be described in detail below.

[0043] Determine the number of drones in the drone cluster; when the number of drones is greater than or equal to the set number threshold, use the arrival time difference method to calculate the measurement position of each drone, and use the Doppler velocity measurement algorithm to calculate the measurement speed of each drone; when the number of drones is less than the set number threshold, use the arrival difference lateral algorithm to calculate the measurement position of each drone, and use the Doppler velocity measurement algorithm to calculate the measurement speed of each drone.

[0044] It is easy to understand that when there are a large number of drones in a drone cluster, the monitoring drone uses the arrival time difference method to locate the drone and uses Doppler speed measurement technology to measure the relative speed of the drone; when there are a small number of drones in a drone cluster, the abnormality of a single drone cannot cause more damage to the cluster, so the number of monitoring drones can be reduced, and an antenna array can be used to determine the direction of a specific drone, and Doppler speed measurement technology can be used to determine the current speed information of the drone.

[0045] Specifically, the number of drones in the swarm is first determined, which is known when the swarm is created. The number of drones is then compared with a set threshold. Based on the comparison result, the method used to calculate the measured speed and position of the drones is determined. The threshold can be set based on actual circumstances and is not specifically defined here.

[0046] For example, in a specific embodiment, the number of drones in the drone cluster is greater than a set threshold. In this case, the measured position of each drone is calculated using the arrival time difference method, and the measured speed of each drone is calculated using the Doppler speed measurement method.

[0047] Specifically, when there are a large number of drones in a drone cluster (greater than or equal to the set number threshold), the monitoring drone uses the Time Difference of Arrival (TDOA) method to calculate the measured position of the drone and uses the Doppler speed measurement technology to calculate the measured speed of the drone.

[0048] Expanded, Figure 2 FIG1 shows one of the calculation diagrams of the measurement position of the UAV provided by the embodiment of the present invention. Figure 2 As shown, assuming that the coordinates of the target drone are , 、 、 、 Respectively represent the distance between the monitoring UAV and the target UAV, then It can be calculated by the following formula (1).

[0049] (1).

[0050] In formula (1), 、 、 、 The coordinates of the four monitoring drones.

[0051] The distance difference formula is as follows (2).

[0052] (2).

[0053] In formula (2), 、 、 、 They represent the time it takes for the signal transmitted by the target UAV to reach the four monitoring UAVs, Indicates the speed of signal propagation.

[0054] Further conversion can obtain the following homogeneous linear equation system (3).

[0055] (3).

[0056] Define the homogeneous linear equation system (3) as ,So ,in, for The inverse matrix of is the coordinate vector of the target UAV, that is, the measured position of the UAV.

[0057] About Doppler velocity measurement algorithm. Figure 3 FIG. 1 shows a schematic diagram of calculating the measured speed of a drone provided by an embodiment of the present invention. Figure 3 As shown, it is assumed that the carrier frequency of the RF signal sent by the target drone is , the relative speed is , the local oscillator frequency of the monitoring drone is , mix and filter the local oscillator frequency, carrier frequency and relative speed to obtain the mixed signal (i.e. Figure 3 pressure difference in ,and 、 is a fixed value, so the mixed signal can be regarded as Related functions.

[0058] In the known and In this case, the relative speed of the target UAV can be solved, that is, the measured speed of the UAV.

[0059] For example, in another specific embodiment, the number of drones in the drone cluster is less than a set threshold. In this case, the arrival difference lateral algorithm is used to calculate the measured position of each drone, and the Doppler speed measurement method is used to calculate the measured speed of each drone.

[0060] Specifically, when the number of drones in a drone cluster is small, the anomaly of a single drone cannot cause more damage to the cluster. Therefore, the number of monitoring drones can be reduced, and an antenna array can be used to determine the measurement position of a specific drone, and Doppler speed measurement technology can be used to determine the current measurement speed of the drone.

[0061] Expanded, Figure 4 FIG2 shows a second schematic diagram of calculating the measurement position of the drone provided by an embodiment of the present invention. Figure 4 As shown, it is assumed that the fixed distance between antenna ANT_A and antenna ANT_B is The time difference between antenna ANT_A and antenna ANT_B is , the azimuth of the target UAV, that is, the measured position of the UAV, can be calculated using the following formula (4).

[0062] (4).

[0063] The Doppler velocity measurement technology is used to determine the current measured speed of the UAV. For details, please refer to the above embodiment and will not be elaborated here.

[0064] In this embodiment, the number of drones in a drone swarm is determined. If the number of drones is greater than or equal to a set threshold, the arrival time difference method is used to calculate the measured position of each drone, and the Doppler velocity algorithm is used to calculate the measured velocity of each drone. If the number of drones is less than the set threshold, the arrival difference lateral algorithm is used to calculate the measured position of each drone, and the Doppler velocity algorithm is used to calculate the measured velocity of each drone. Based on the broadcast information, the measured positions, and the measured velocities, abnormal drones in the drone swarm are identified and handled. This method can quickly and accurately detect abnormal drones in a drone swarm and, by handling the abnormal drones detected, avoid further damage.

[0065] Based on the above embodiment, the process of determining abnormal drones will be described in detail below.

[0066] Based on the broadcast information, the measured position and the measured speed, abnormal drones in the drone cluster are determined, including: comparing the position information in the broadcast information with the measured position to obtain a position difference; comparing the speed information in the broadcast information with the measured speed to obtain a speed difference; when the position difference is higher than a first set difference and / or the speed difference is higher than a second set difference, the drone corresponding to the broadcast information is determined as an abnormal drone.

[0067] It is easy to understand that by comparing the position information in the broadcast information with the measured position, a position difference value can be obtained, and by comparing the speed information in the broadcast information with the measured speed, a speed difference value can be obtained. Furthermore, the position difference value is compared with a first set difference value to obtain a position comparison result, and the speed difference value is compared with a second set difference value to obtain a speed comparison result.

[0068] If the position comparison result shows that the position difference is greater than the first set difference, the drone corresponding to the broadcast information is determined to be an abnormal drone. If the position comparison result shows that the position difference is less than or equal to the first set difference, the drone corresponding to the broadcast information is in a normal operating state.

[0069] If the speed comparison result shows that the speed difference is greater than the second set difference, the drone corresponding to the broadcast information is determined to be an abnormal drone. If the speed comparison result shows that the speed difference is less than or equal to the second set difference, the drone corresponding to the broadcast information is in a normal operating state.

[0070] That is to say, when any one of the position difference and speed difference, or both, are higher than the corresponding set difference, the drone corresponding to the broadcast information is an abnormal drone.

[0071] The first set difference and the second set difference can be set according to actual conditions and are not specifically limited here.

[0072] In this embodiment, the position information in the broadcast information is compared with the measured position to obtain a position difference, and the speed information in the broadcast information is compared with the measured speed to obtain a speed difference. If the position difference is greater than a first set difference and / or the speed difference is greater than a second set difference, the drone corresponding to the broadcast information is identified as an abnormal drone, and abnormality treatment is then performed on the abnormal drone. This method can quickly and accurately detect abnormal drones in a drone cluster, and by performing abnormal treatment on detected abnormal drones, further damage is avoided.

[0073] Based on the above embodiments, the following further describes in detail the abnormal handling of abnormal drones.

[0074] The steps for handling abnormal drones specifically include: when the abnormal drone's power is normal, taking over the abnormal drone's motor control through additionally allocated computing resources, and no longer using the abnormal drone's sensor information; when the abnormal drone's power is abnormal, or the abnormal drone remains abnormal after being taken over, abandoning the abnormal drone and determining the abnormal drone's movement path to control other drones in the drone cluster to leave the area where the abnormal drone's movement path is located.

[0075] It is easy to understand that when an abnormal drone is detected in a drone cluster, additional measures should be taken to control the abnormal drone to ensure the safe and stable operation of the entire drone cluster.

[0076] Specifically, the anomalous drone is flagged and reported to the master drone. The master drone then determines specific instructions based on the anomalous drone's battery level. If the anomalous drone's battery level is normal, additional computing resources can be allocated to take over motor control of the anomalous drone, bypassing the anomalous drone's sensor information. This means the master drone no longer relies on any sensor information provided by the anomalous drone to make decisions. Instead, it directly sends instructions to the anomalous drone's motor control system based on pre-set target position, speed, or other parameters. This approach can quickly correct anomalous behavior, prevent the situation from escalating, and give the anomalous drone an opportunity to resume normal operations.

[0077] Furthermore, after the abnormal drone is taken over, the master drone will continue to closely monitor its status changes. If the abnormal drone's abnormal status does not improve after a period of time, it means that the takeover strategy has failed. In this case, the master drone will abandon intervention on the abnormal drone and turn to protecting other drones from its influence.

[0078] The master drone will use the last known location, speed, and direction of the anomalous drone, combined with environmental conditions (wind speed, terrain, and other factors), to predict the anomalous drone's likely next movement path. Based on the predicted movement path, the master drone will then command other drones located near or near the path to immediately change course or temporarily hover, ensuring they avoid potentially dangerous areas.

[0079] At the same time, it is also possible to consider replanning the mission and adjusting the working layout of the entire cluster to compensate for the decline in mission execution capability caused by the loss of abnormal drones.

[0080] It should be noted that the above "taking over control" to "giving up" is a gradual progressive strategy. If the battery level of the abnormal drone is abnormal from the beginning, the master drone will directly "give up" the abnormal drone.

[0081] The battery level of an abnormal drone can be obtained based on the battery status in its broadcast information. Whether the battery level is abnormal can be determined by comparing the remaining battery level with the battery threshold. If the battery level of an abnormal drone is lower than the battery threshold, it is abnormal; otherwise, the battery level is normal.

[0082] In this embodiment, if the abnormal drone's battery level is normal, additionally allocated computing resources are used to take over the abnormal drone's motor control, eliminating the use of the abnormal drone's sensor information. If the abnormal drone's battery level is abnormal, or if the abnormal drone remains abnormal after being taken over, the abnormal drone is abandoned and its motion path is determined, thereby controlling the other drones in the drone cluster to move away from the abnormal drone's motion path. This method, by directly controlling the abnormal drone after detecting it and bypassing sensors, and controlling other drones to avoid the abnormal drone, can prevent further damage.

[0083] On the basis of the above embodiment, further, each drone in the drone cluster has a self-monitoring function, and the broadcast information also includes self-test results; accordingly, the broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster is received, which includes: obtaining the drone's own power data and sensor data; judging whether the drone itself has an abnormality based on the power data and sensor data, and obtaining the self-test results in the broadcast information; wherein the self-test results in the broadcast information include normal status or abnormal status.

[0084] It's easy to understand that all drones in a swarm have self-monitoring capabilities. They monitor their battery life and sensor data to determine whether their motion is abnormal (comparing their battery life to corresponding battery thresholds and their sensor data to corresponding sensor data thresholds). These self-test results are then broadcast along with their speed and position information as a signal indicating whether their status is normal or abnormal.

[0085] Furthermore, when the self-test result shows an abnormal state, the drone is directly processed for abnormality based on the self-test result. The specific abnormality processing steps can be referred to the above embodiment and will not be expanded here.

[0086] If the self-test results in a normal state, steps S110-S120 are executed. Specifically, the broadcast information is received and the corresponding measured position and speed are calculated. Based on the broadcast information, the measured position and speed, abnormal drones in the drone cluster are identified and handled. The specific execution steps can be found in the above embodiment and will not be elaborated on here.

[0087] In this embodiment, the drone's own power and sensor data are obtained, and based on this data, the drone's own abnormality is determined, resulting in the self-test result in the broadcast message. If the self-test result indicates an abnormal state, the drone is handled according to the self-test result. If the self-test result indicates a normal state, the broadcast message is received and the corresponding measurement position and speed are calculated. Based on the broadcast information, the measurement position, and the measurement speed, the abnormal drone in the drone cluster is identified and handled. This method can avoid further damage by bypassing the sensors after detecting the abnormal drone and directly controlling the abnormal drone, and controlling other drones to avoid the abnormal drone.

[0088] In other embodiments, Figure 5 The overall process diagram of the drone cluster anomaly monitoring method provided by the embodiment of the present invention is shown. Figure 5 As shown, each drone first performs a self-test using its self-monitoring function and obtains a self-test result. This self-test result is then included in a broadcast message. After receiving the broadcast message, the monitoring drone parses the self-test result. If the self-test result indicates an abnormal state, the corresponding drone is identified as an abnormal drone and undergoes abnormality processing. If the self-test result indicates a normal state, the measured position and speed of each drone are calculated. The speed and position information in the broadcast message are then compared with the measured speed and position, respectively, to determine whether each drone is abnormal. If a drone is determined to be abnormal, it is identified as an abnormal drone and undergoes abnormality processing.

[0089] Corresponding to the drone cluster anomaly monitoring method described in the above embodiments, the present invention also provides a drone cluster anomaly monitoring device. Specifically, Figure 6 The figure shows a schematic structural diagram of a drone cluster anomaly monitoring device provided by an embodiment of the present invention.

[0090] like Figure 6 As shown, the device includes: an information receiving and calculating module 610, which is used to receive broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculate the measured position and measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; an abnormality monitoring and processing module 620, which is used to determine the abnormal drone in the drone cluster based on the broadcast information, the measured position and the measured speed, and perform abnormal processing on the abnormal drone.

[0091] In this embodiment, the information receiving and calculation module 610 receives broadcast information from each drone in the drone cluster in a sequential, time-sharing manner and calculates the measured position and speed of each drone. The broadcast information includes the position and speed of each drone. The anomaly monitoring and processing module 620 identifies abnormal drones in the drone cluster based on the broadcast information, measured positions, and measured speeds, and performs an abnormality treatment on the abnormal drones. This device can quickly and accurately detect abnormal drones in the drone cluster and, by performing an abnormality treatment on the detected abnormal drones, avoid further damage.

[0092] Figure 7 An example of a physical structure diagram of an electronic device is shown below. Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may call logic instructions in the memory 730 to execute a method for monitoring abnormalities in a drone cluster, the method comprising: receiving broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculating the measured position and measured speed of each drone; wherein the broadcast information includes the position and speed information of each drone; based on the broadcast information, the measured position, and the measured speed, determining abnormal drones in the drone cluster, and performing abnormal processing on the abnormal drones.

[0093] Furthermore, the logic instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0094] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the drone cluster abnormality monitoring method provided by the above methods, which includes: receiving broadcast information broadcasted sequentially and in time by each drone in the drone cluster, and calculating the measurement position and measurement speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; based on the broadcast information, the measurement position and the measurement speed, determining the abnormal drone in the drone cluster, and performing abnormal processing on the abnormal drone.

[0095] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the drone cluster abnormality monitoring method provided by the above-mentioned methods. The method includes: receiving broadcast information broadcasted sequentially and in time by each drone in the drone cluster, and calculating the measurement position and measurement speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; based on the broadcast information, the measurement position and the measurement speed, determining the abnormal drone in the drone cluster, and performing abnormal processing on the abnormal drone.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0097] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring abnormalities in a drone cluster, characterized in that: include: Receive broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculate the measured position and measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; Based on the broadcast information, the measurement position and the measurement speed, abnormal drones in the drone cluster are determined, and abnormal processing is performed on the abnormal drones.

2. The method for monitoring abnormality of drone clusters according to claim 1, characterized in that: The step of calculating the measurement position and measurement speed of each drone specifically includes: determining the number of drones in the drone swarm; When the number of the drones is greater than or equal to a set number threshold, the measured position of each drone is calculated using the arrival time difference method, and the measured speed of each drone is calculated using the Doppler speed measurement algorithm; When the number of the UAVs is less than a set number threshold, the arrival difference lateral algorithm is used to calculate the measured position of each UAV, and the Doppler velocity measurement algorithm is used to calculate the measured speed of each UAV.

3. The method for monitoring abnormality of drone clusters according to claim 1, characterized in that: The determining, based on the broadcast information, the measured position, and the measured speed, an abnormal drone in the drone cluster includes: Comparing the location information in the broadcast information with the measured location to obtain a location difference; comparing the speed information in the broadcast information with the measured speed to obtain a speed difference; When the position difference is higher than a first set difference, and / or the speed difference is higher than a second set difference, the drone corresponding to the broadcast information is determined as the abnormal drone.

4. The method for monitoring abnormality of drone clusters according to claim 1, characterized in that: The step of performing abnormal processing on the abnormal drone specifically includes: If the power level of the abnormal drone is normal, the motor control of the abnormal drone is taken over by using additional allocated computing resources, and the sensor information of the abnormal drone is no longer used; In the event that the power of the abnormal drone is abnormal, or the abnormal drone remains abnormal after being taken over for control, the abnormal drone is abandoned, and the movement path of the abnormal drone is determined to control other drones in the drone cluster to leave the area where the movement path of the abnormal drone is located.

5. The method for monitoring abnormalities of drone clusters according to any one of claims 1 to 4, characterized in that: Each drone in the drone cluster has a self-monitoring function, and the broadcast information also includes self-monitoring results; Accordingly, the receiving of broadcast information broadcasted sequentially and time-divided by each drone in the drone cluster includes: Obtain the drone's own power data and sensor data; Determine whether the drone itself has an abnormality based on the power data and sensor data, and obtain the self-test result in the broadcast information; The self-test result in the broadcast information includes a normal status or an abnormal status.

6. The method for monitoring abnormalities of drone clusters according to claim 5, characterized in that: If the self-test result indicates an abnormal state, performing abnormal processing on the drone according to the self-test result; When the self-test result is normal, receiving the broadcast information and calculating the corresponding measurement position and measurement speed; Based on the broadcast information, the measurement position and the measurement speed, abnormal drones in the drone cluster are determined, and abnormal processing is performed on the abnormal drones.

7. A drone cluster abnormality monitoring device, characterized in that: include: An information receiving and calculating module, configured to receive broadcast information broadcasted sequentially and time-sharingly by each drone in the drone cluster, and calculate the measured position and measured speed of each drone; wherein the broadcast information includes the position information and speed information of each drone; The abnormality monitoring and processing module is used to determine abnormal drones in the drone cluster based on the broadcast information, the measurement position and the measurement speed, and perform abnormal processing on the abnormal drones.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the drone cluster anomaly monitoring method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring abnormalities of a drone cluster as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for monitoring abnormalities of a drone cluster as described in any one of claims 1 to 6 is implemented.

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