WO2020216072A1 - 无人机异常坠地的检测方法、装置、设备以及存储介质 - Google Patents

无人机异常坠地的检测方法、装置、设备以及存储介质 Download PDF

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WO2020216072A1
WO2020216072A1 PCT/CN2020/084124 CN2020084124W WO2020216072A1 WO 2020216072 A1 WO2020216072 A1 WO 2020216072A1 CN 2020084124 W CN2020084124 W CN 2020084124W WO 2020216072 A1 WO2020216072 A1 WO 2020216072A1
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Prior art keywords
ground
telemetry data
parameter
data
drone
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PCT/CN2020/084124
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English (en)
French (fr)
Inventor
尹亮亮
吕萌
张羽
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拓攻（南京）机器人有限公司
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Priority to JP2021564125A priority Critical patent/JP7297332B2/ja
Publication of WO2020216072A1 publication Critical patent/WO2020216072A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft

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  • the embodiments of the present application relate to the technical field of drones, for example, to a detection method, device, equipment, and storage medium for abnormally falling of a drone to the ground.
  • UAVs are gradually being used in agriculture, forestry, aerial photography, surveying and mapping, and inspections.
  • a bomber refers to an unmanned aerial vehicle falling abnormally to the ground due to factors such as improper operation or machine failure.
  • UAVs that fall abnormally on the ground are completely scrapped and difficult to use again, causing great losses to users.
  • manufacturers usually introduce compensation measures to encourage users to use drones. For example, when it is judged that the drone is unusable due to an abnormal fall, the user can choose to return the drone after the abnormal fall to the manufacturer.
  • the manufacturer takes back the drone and sells the same model of drone to the user at a price lower than the market price to reduce the user's loss, or return some maintenance funds to the user.
  • the maintenance fund is used to repair or purchase new machines.
  • the embodiments of the present application provide a detection method, device, equipment and storage medium for abnormal landing of a drone to optimize the detection method of abnormal landing of a drone in the related art, and accurately judge whether a drone has exploded and avoid Human misjudgment improves the efficiency of judgment.
  • an embodiment of the present application provides a method for detecting abnormal landing of a drone, including:
  • the embodiment of the present application also provides a device for detecting abnormal landing of a drone, including:
  • the data acquisition module is configured to acquire the telemetry data of the drone to be detected
  • the data extraction module is configured to extract effective ground-off telemetry data from the telemetry data
  • the UAV detection module is configured to detect whether the UAV belongs to the abnormal landing according to the preset parameter conditions for abnormal landing and the parameter values of the attitude related parameters and the component related parameters determined by the ground-off telemetry data .
  • an embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor implements any of the embodiments of the present application when the computer program is executed.
  • the provided detection method for the abnormal landing of the drone is not limited to a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • an embodiment of the present application also provides a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium.
  • the computer program is executed by a processor, the drone provided in any embodiment of the present application is implemented. Detection method of abnormal fall.
  • FIG. 1 is a flowchart of a method for detecting abnormal landing of a drone provided by Embodiment 1 of the application;
  • FIG. 2 is a flowchart of a method for detecting abnormal landing of a UAV according to the second embodiment of the application;
  • FIG. 3 is a flowchart of a method for detecting abnormal landing of a drone provided by the third embodiment of the application;
  • FIG. 4 is a structural block diagram of a detection device for abnormal landing of a drone provided by the fourth embodiment of the application;
  • FIG. 5 is a schematic structural diagram of a computer device provided in Embodiment 5 of this application.
  • FIG. 1 is a flowchart of a method for detecting abnormal landing of a drone according to the first embodiment of the application. This embodiment is applicable to situations where the method can be executed by a detection device for abnormal landing of a drone.
  • the device is executed by software and/or hardware, and generally can be integrated in a computer device, such as a server or a terminal device. As shown in Figure 1, the method may include the following steps:
  • Step 101 Obtain telemetry data of the drone to be detected.
  • the telemetry data is collected by the sensor module of the drone, and the flight data received by the server comes from the drone and can reflect the digital characteristics or status of the drone.
  • the drone can include a sensor module, a data recording device, and a flight control module.
  • the sensor module of the UAV is used to collect the flight data of the UAV.
  • the sensor module may include a three-axis accelerometer, a three-axis gyroscope, a magnetic compass, a barometer, a global positioning system (Global Positioning System, GPS), a voltage detection unit, and a current detection unit.
  • the flight data can include three-axis attitude angle, angular rate, acceleration, position, satellite state data when the UAV is flying, and the voltage and current of the components.
  • the sensor module transmits the collected flight data to the flight control module through the local bus.
  • the flight control module receives the flight data collected by the sensor module and the control commands from the user, processes the fusion and generates the flight instructions and sends them to the actuators.
  • the drone flies, and at the same time sends the received flight data and control instructions to the data recording device.
  • the data recording device is used to store user control instructions and flight data of the sensor module, and send the user control instructions and flight data of the sensor module to the server.
  • the data recording device may include a main board and a processor, a storage module and a wireless communication module integrated on the main board, the processor is connected with the flight control module, the storage module and the wireless communication module are respectively connected with the processor, and the wireless communication module communicates with the server.
  • the remote measurement data of the UAV can be saved through the server, which provides a basis for subsequent analysis of the flight status of the UAV.
  • the drone to be detected may be a drone that needs to be judged whether it is an abnormal fall to the ground.
  • the data of each drone can be recorded into multiple corresponding files.
  • the content of each file can be different according to the recording method.
  • the recording method is recording according to power-on.
  • the data of the drone during a power-on process is recorded as a file.
  • This file can include the telemetry data of the drone during a power-on process.
  • the recording method is to record according to the number of landings.
  • the file can include telemetry data from a drone flight.
  • the process of unlocking and locking the drone is one sortie.
  • the unlocked status flag in the flight status parameters of the drone indicates whether the drone is locked or unlocked. According to the value of the unlock status flag, it can be detected whether the drone is locked or unlocked, and when it is detected that the drone is locked, the data of the drone will be recorded as a file, and the subsequent data will be recorded in Among the new files.
  • the server can obtain the telemetry data of the drone to be detected in the file, and accurately judge whether the drone has fallen to the ground abnormally based on the drone's telemetry data.
  • Step 102 Extract effective ground-off telemetry data from the telemetry data.
  • the data integrity of the telemetry data may be first detected.
  • the ground-off telemetry data in the telemetry data is obtained. If the remote sensing data is incomplete, you can no longer judge whether the drone to be detected is an abnormal fall based on the remote sensing data, and directly determine whether the drone to be detected is an abnormal fall.
  • the step of detecting the data integrity of the telemetry data includes: judging whether the telemetry data includes a number of satellites, a roll angle, a pitch angle, and a motor speed parameter.
  • the number of satellites parameter is the number of satellites when the UAV is flying.
  • the satellite status of the UAV to be detected during flight can be verified.
  • the step of verifying the satellite status of the drone to be detected during flight may include: determining the number of satellites during the flight of the drone to be detected according to the satellite number parameter, and then determining that the drone to be detected is indoor according to the number of satellites Flying or flying outdoors.
  • the roll angle parameter is the roll angle of the drone.
  • the pitch angle parameter is the pitch angle of the drone.
  • the motor speed parameter is the motor speed of each motor of the drone. If any one of the satellite quantity parameter, roll angle parameter, pitch angle parameter, and motor speed parameter is missing in the telemetry data, it is determined that the telemetry data is incomplete.
  • the ground-off telemetry data refers to the telemetry data of the unmanned aerial vehicle taking off and off the ground to be detected.
  • the method for obtaining the ground clearance data may be: intercept the ground clearance telemetry data in the telemetry data according to the ground clearance mark in the flight status parameter corresponding to the telemetry data. If there is no ground-off telemetry data in the telemetry data, it can be directly determined that the drone to be detected does not belong to an abnormal fall to the ground. If there is ground-off telemetry data in the telemetry data, intercept the ground-off telemetry data in the telemetry data, and judge whether the ground-off telemetry data meets the data validity condition.
  • the ground-off telemetry data is determined as valid ground-off telemetry data. If the ground-off remote sensing data does not meet the data validity conditions, it is no longer possible to determine whether the drone to be detected is an abnormal fall based on the ground-off remote sensing data, and directly determine whether the drone to be detected is an abnormal fall.
  • the step of judging whether the off-ground telemetry data meets the data validity condition may include: judging the fifth off-ground telemetry in which the number of satellites in the off-ground telemetry data is greater than the preset number of satellites according to the number of satellites corresponding to the off-ground telemetry data Whether the ratio of the amount of data to the total amount of remote data from the ground is greater than the preset ratio threshold; when the ratio is determined to be greater than the preset ratio threshold, judge whether the drone is a simulator based on the battery voltage parameters corresponding to the ground telemetry data ; When it is determined that the drone is not a simulator, determine whether the drone has been dismantled (that is, the propeller has been dismantled) according to the motor speed parameters corresponding to the ground-off telemetry data; The ground telemetry data meets the data validity conditions.
  • Step 103 According to the at least one posture related parameter determined from the ground-off telemetry data, the parameter value of the at least one component related parameter, and the preset parameter conditions for the abnormal fall to the ground, it is detected whether the drone is an abnormal fall.
  • the attitude related parameters may include: a roll angle parameter and a pitch angle parameter.
  • the roll angle parameter and the pitch angle parameter are the roll angle and pitch angle of the UAV.
  • the component-related parameters can be motor speed parameters.
  • the motor speed parameter is the motor speed of each motor of the drone.
  • the step of detecting whether the UAV belongs to the abnormal landing according to at least one attitude related parameter determined by the ground-off telemetry data, the parameter value of the at least one component related parameter, and the preset parameter conditions for the abnormal landing match Including: judging whether the drone meets the preset first parameter condition for abnormal landing matching according to the roll angle parameter corresponding to the ground-off telemetry data; determining that the drone does not meet the preset first parameter condition for abnormal landing matching When determining whether the drone meets the preset second parameter condition for abnormal landing matching according to the pitch angle parameters corresponding to the ground-off telemetry data; when it is determined that the drone does not meet the preset second parameter condition for abnormal landing matching , According to the sorting parameters corresponding to the ground-off telemetry data, for each sortie, according to the roll angle parameters, pitch angle parameters, and motor speed parameters corresponding to the ground-off telemetry data in the sorties, judge whether the UAV meets the preset abnormal landing match The third parameter condition. Therefore, it is determined whether the UAV belongs to an attitude related parameter determined
  • This embodiment provides a method for detecting abnormal landing of a drone on the ground. It acquires telemetry data with the drone to be detected, and extracts effective ground-off telemetry data from the telemetry data, and then based on the ground-off telemetry data Determined at least one attitude-related parameter, at least one component-related parameter parameter value, and preset parameter conditions for abnormally falling to the ground, to detect whether the drone is abnormally falling to the ground, and to solve the inconsistent judgment standard , Can not give an accurate judgment, and the judgment efficiency is too low. According to the remote measurement data of the drone, it can be more accurately judged whether the drone has abnormally fallen to the ground, and a more unified judgment standard is given to avoid human error. To improve the efficiency of judgment.
  • Fig. 2 is a flowchart of a method for detecting abnormal landing of a drone provided by the second embodiment of the application.
  • the attitude related parameters include: roll angle parameters and pitch angle parameters; component related parameters are motor speed parameters.
  • the step of extracting effective ground-off telemetry data may include: judging whether the telemetry data includes satellite quantity parameters, roll angle parameters, and pitch angle parameters; determining the telemetry data includes satellite quantity parameters, roll angle parameters, and When the pitch angle parameter is used, the ground-off telemetry data in the telemetry data is obtained; it is judged whether the ground-off telemetry data meets the data validity conditions; when it is determined that the ground-off telemetry data meets the data validity conditions, the ground-off telemetry data is determined as effective ground-off telemetry data.
  • the method may include the following steps:
  • Step 201 Obtain telemetry data of the drone to be detected.
  • Step 202 Determine whether the telemetry data includes satellite quantity parameters, roll angle parameters, and pitch angle parameters.
  • any one of the satellite quantity parameter, roll angle parameter, and pitch angle parameter is missing in the telemetry data, it can be determined that the telemetry data is incomplete, and the remote sensing data is no longer used to determine whether the drone to be detected is If the drone falls abnormally to the ground, it is directly determined that the drone to be detected does not belong to the drone that falls abnormally to the ground.
  • Step 203 When it is determined that the telemetry data includes the satellite quantity parameter, the roll angle parameter, and the pitch angle parameter, obtain the ground-off telemetry data in the telemetry data.
  • the ground-off telemetry data in the telemetry data can be intercepted according to the ground-off flag in the flight state parameters corresponding to the telemetry data.
  • the ground clearance flag in the flight status parameter corresponding to the telemetry data is 0, it means that the drone has not taken off and leaves the ground, and the telemetry data is not ground clearance telemetry data; if the flight status parameter corresponding to the telemetry data is The ground clearance flag of is not 0, which means that the UAV takes off from the ground, and the telemetry data is ground clearance telemetry data.
  • Step 204 Judge whether the remote sensing data from the ground meets the data validity condition.
  • the number of ground-level telemetry data is greater than the preset number of satellites in the fifth ground-level telemetry data and the total amount of ground-level telemetry data. Whether the ratio is greater than the preset ratio threshold.
  • the preset number of satellites and the preset ratio threshold can be set according to business requirements. If the ratio is determined to be greater than the preset ratio threshold, then continue to judge the validity of the data according to the battery voltage parameters corresponding to the ground telemetry data; if the ratio is determined to be less than or equal to the preset ratio threshold, it is determined that the ground telemetry data does not meet the data validity conditions.
  • the preset number of satellites is 4, and the preset ratio threshold is 0.7.
  • the data quantity of ground-off telemetry data with the number of satellites greater than 4 is determined.
  • a ratio greater than 0.7 indicates that the drone is indoors; a ratio less than or equal to 0.7 indicates that the drone is outdoors.
  • the drone When it is determined that the ratio is greater than the preset ratio threshold, it can be judged whether the drone is a simulator based on the battery voltage parameters corresponding to the ground-based telemetry data.
  • the battery voltage parameter is the UAV battery voltage in V. If the battery voltage parameters corresponding to each ground-off telemetry data are all 0, or the battery voltage parameters corresponding to each ground-off telemetry data are the same value, that is, the UAV battery voltage always remains the same, then the UAV is determined to be simulated Detector to determine that the ground-off telemetry data does not meet the data validity conditions. In other cases, it can be determined that the UAV is not a simulator, and continue to determine the validity of the data based on the motor speed parameters corresponding to the ground telemetry data.
  • the motor speed parameter is the motor speed of the drone motor.
  • the drone can have up to 8 motors.
  • the drone can have 4 motors, 6 motors or 8 motors.
  • a drone has 8 motors.
  • there are motor speed parameters corresponding to each motor that is, there are 8 motor speed parameters in the ground-off remote measurement data at each moment.
  • the minimum value of the motor speed parameter corresponding to each ground-lift telemetry data is obtained as a minimum combination.
  • the minimum motor speed parameter is the smallest motor speed parameter among the multiple motor speed parameters included in the ground-off telemetry data.
  • filter out motor speed parameters with a value greater than 0 and record the number of motor speed parameters with a value greater than 0 as sum. Filter out the data with a value less than or equal to 12 from the motor speed parameters with a value greater than 0, and record the number of data with a value less than or equal to 12 as num.
  • num/sum is greater than 0.12, the drone is determined to be dismantled, and the ground-off telemetry data does not meet the data validity conditions; if num/sum is less than or equal to 0.12, it is determined that the drone has not been dismantled, and the ground-off telemetry data is determined Meet the data validity conditions.
  • Both 12 and 0.12 are adjustable parameters, which can be adjusted according to business needs.
  • Step 205 When it is determined that the ground-off telemetry data meets the data validity condition, the ground-off telemetry data is determined as valid ground-off telemetry data.
  • Step 206 According to the at least one posture related parameter determined from the ground-off telemetry data, the parameter value of the at least one component related parameter, and the preset parameter conditions for the abnormal fall to the ground, it is detected whether the drone is an abnormal fall.
  • the method for detecting abnormal landing of a drone is to obtain the ground-off telemetry data in the telemetry data when it is determined that the telemetry data includes the satellite quantity parameter, the roll angle parameter, and the pitch angle parameter, and determine the ground-off Whether the telemetry data meets the data validity conditions, when it is determined that the ground telemetry data meets the data validity conditions, the ground telemetry data is determined to be valid ground telemetry data. According to the parameters corresponding to the telemetry data, the effective data can be extracted from the telemetry data. Telemetry data from the ground.
  • Fig. 3 is a flowchart of a method for detecting an abnormal landing of a drone provided by the third embodiment of the application.
  • the parameter value of at least one component-related parameter is determined based on at least one attitude-related parameter determined from ground-off telemetry data
  • the preset parameter conditions for abnormal fall to the ground may include: judging whether the drone meets the preset abnormal fall matching according to the roll angle parameter corresponding to the ground-off telemetry data The first parameter condition; when it is determined that the UAV does not meet the preset first parameter condition for abnormal landing matching, according to the pitch angle parameter corresponding to the ground-off telemetry data, it is judged whether the UAV meets the preset abnormal landing matching first parameter.
  • Two parameter conditions when it is determined that the UAV does not meet the preset second parameter condition for abnormal landing matching, for each sortie, according to the roll angle parameter, pitch angle parameter, and motor speed corresponding to the ground-off telemetry data in the sortie.
  • the parameter determines whether the UAV meets the preset third parameter condition of abnormal landing match.
  • the method may include the following steps:
  • Step 301 Obtain telemetry data of the drone to be detected.
  • Step 302 Extract effective ground-off telemetry data from the telemetry data.
  • Step 303 Determine whether the UAV meets the preset first parameter condition for abnormal landing matching according to the roll angle parameter corresponding to the ground-off telemetry data.
  • the first ground-off telemetry data whose roll angle parameter is greater than the first roll-angle threshold in the ground-off telemetry data is acquired. That is, the first ground clearance telemetry data is ground clearance telemetry data whose roll angle parameter is greater than the first roll angle threshold.
  • the second ground-off telemetry data is ground clearance telemetry data whose roll angle parameter is greater than the second roll angle threshold.
  • the first roll angle threshold is 90° (90° is an adjustable parameter).
  • the second roll angle threshold is 50° (50° is an adjustable parameter).
  • any one of the first ground-off telemetry data belongs to the last 3 telemetry data in all the telemetry data, it is determined that the drone meets the preset first parameter condition for abnormal landing matching, and the drone is abnormal Fall to the ground.
  • 3 is an adjustable parameter. Any one of the first ground-off telemetry data belongs to the last three telemetry data in all the telemetry data, which means that after the drone fell abnormally to the ground, the data transmission was interrupted, resulting in the lack of subsequent data. That is, in the last three telemetry data of all telemetry data, there are telemetry data with a roll angle parameter greater than 90°, which is most likely to be rolled over. Combined with the interruption of subsequent data, it is determined that the UAV actually rolled over.
  • ground-off telemetry data in the first ground-off telemetry data does not belong to the last three telemetry data in all the telemetry data, continue to check the first 6 of the sorted positions of the ground-off telemetry data in all the telemetry data
  • One telemetry data and the last six telemetry data (6 are adjustable parameters). It is judged whether there is telemetry data with a roll angle parameter greater than the third roll angle threshold among the first 6 telemetry data and the last 6 telemetry data in the sorting position where the ground-off telemetry data is located.
  • the third roll angle threshold is 40° (40° is an adjustable parameter).
  • Step 304 When it is determined that the drone does not meet the preset first parameter condition for abnormal landing matching, judge whether the drone meets the preset second parameter for abnormal landing matching according to the pitch angle parameter corresponding to the ground-off telemetry data condition.
  • the third ground-off telemetry data whose pitch angle parameter is greater than the first pitch angle threshold in the acquired ground-off telemetry data is ground-lift telemetry whose pitch angle parameter is greater than the first pitch angle threshold data.
  • the fourth ground-off telemetry data whose pitch angle parameter is less than the second pitch-angle threshold in the ground-off telemetry data that is, the fourth ground-off telemetry data is the ground-off telemetry data whose pitch angle parameter is greater than the second pitch-angle threshold.
  • the first pitch angle threshold is 90° (90° is an adjustable parameter).
  • the second pitch angle threshold is 50° (50° is an adjustable parameter).
  • any one of the third ground-off telemetry data belongs to the last 3 telemetry data in all the telemetry data, it is determined that the drone meets the preset second parameter condition of abnormal landing matching, and the drone is abnormal Fall to the ground.
  • 3 is an adjustable parameter. Any one of the third ground-off telemetry data belongs to the last three telemetry data in all the telemetry data, which means that after the drone fell to the ground abnormally, the data transmission was interrupted, resulting in the lack of subsequent data.
  • the ground-off telemetry data in the third ground-off telemetry data does not belong to the last 3 telemetry data in all the telemetry data, continue to check the top 6 of the sorted position of the ground-off telemetry data in all the telemetry data
  • One telemetry data and the last six telemetry data (6 are adjustable parameters).
  • the third pitch angle threshold is 40° (40° is an adjustable parameter).
  • Step 305 When it is determined that the drone does not meet the preset second parameter condition for abnormal landing matching, for each sortie, according to the roll angle parameter, pitch angle parameter, and motor speed parameter corresponding to the ground-off telemetry data in the sortie It is judged whether the UAV meets the preset third parameter condition of abnormal landing match.
  • the process from unlocking the drone to landing and locking is called a sortie.
  • the unlocked status flag in the flight status parameters of the drone indicates whether the drone is locked or unlocked. According to the value of the unlock status flag, it can be detected whether the drone is locked or unlocked. According to the value of the unlocked state flag, the data of each sortie can be extracted from the full amount of data, which is directly based on the ground-off telemetry data of each sortie for further analysis, according to the roll angle parameter corresponding to the ground-off telemetry data in the sortie , Pitch angle parameters, and motor speed parameters to determine whether the UAV meets the preset third parameter condition for abnormal landing matching.
  • the ground-off telemetry data of each sortie is sorted in chronological order, the first 15 ground-off telemetry data of each sortie are removed, and the remaining ground-level telemetry data is used as the sorting data of each sortie.
  • 15 is an adjustable parameter.
  • the purpose of removing the first 15 ground-off telemetry data of each sortie is to remove the ground-off telemetry data with unstable motor speed at the beginning of takeoff.
  • the data number of the flight data of the current flight is less than 2, it is considered that the data volume of the flight data is too small, and the judgment is not made, and the judgment of the next flight is continued. If the number of data of the current order data is greater than 2, then continue the following judgment.
  • the ground telemetry data whose roll angle parameter is greater than the second roll angle threshold is recorded as r.
  • the ground telemetry data whose pitch angle parameter is greater than the second pitch angle threshold is recorded as pitch.
  • r and pitch are ground-off telemetry data suspected to roll over.
  • the motor speed parameters in the maximum combination are sorted in chronological order. Perform a clustering algorithm on the maximum combination, and divide the motor speed parameters in the maximum combination into 2 categories (divide the motor speed parameters in the maximum combination into 2 categories according to the size, and the values of each category are relatively close ), the first classification and the second classification are obtained, and the center point V1 of the first classification and the center point V2 of the second classification are obtained.
  • the center point is the average value of the data in the corresponding category.
  • both V1 and V2 are greater than 90, it is determined that the UAV meets the preset third parameter condition of abnormal landing match, and the UAV belongs to the abnormal landing. Both V1 and V2 are greater than 90, which means that the motor speed is always very high. This is not the case in normal flight. It is the situation that the UAV drops to the ground abnormally shortly after taking off.
  • V1 and V2 are less than 20, and the other value is greater than 90, it is determined that the drone meets the preset third parameter condition of abnormal landing matching, and the drone is an abnormal landing.
  • One of the values of V1 and V2 is less than 20, and the other value is greater than 90, which represents the situation that the drone fell to the ground abnormally shortly after taking off. At the beginning, the average maximum motor speed of the drone was less than 20, and then fell abnormally, and the average maximum motor speed was greater than 90.
  • both V1 and V2 are greater than 25, and the difference between the two is greater than 20 (25 and 20 are adjustable parameters), then check the motor speed parameters (that is, abnormal data) in the maximum value combination in the category with the larger value in turn The sort position. If the abnormal data is in the first half of the maximum combination, skip; if the sort position of the abnormal data is 15 bits different from the sort position of the previous abnormal data, skip; if an abnormal data is in the second half of the maximum combination, the same If the difference between the sorting position of the abnormal data and the sorting position of the last abnormal data is within 15 digits, it is determined that the drone meets the preset third parameter condition of abnormal landing matching, and the drone is an abnormal landing. 15 is an adjustable parameter.
  • the 15-bit difference in judgment is mainly to shield the interference data of sudden change.
  • the interference data is often one piece, and the abnormal data of abnormal fall is often clustered.
  • the difference of the motor speed parameters will not be so different.
  • the existence of such a big difference indicates that there may be an abnormality.
  • the motor speed parameter with a large value may be abnormal data.
  • the abnormal data during an abnormal fall is less than the normal data, that is, the number of data of the motor speed parameter in the category with a larger value may be relatively small.
  • the last data is the last 30 ground-off telemetry data of the current sorties.
  • 30 is an adjustable parameter. Get the maximum motor speed parameter corresponding to the end data of the current flight, and record it as max. If max is greater than or equal to 90, and the difference between max and V1, and the difference between max and V2 are all greater than 10, and the maximum value of the motor speed parameter corresponding to each ground-lift telemetry data in the end data, the value is greater than max-5 If the number of data with the maximum value of the motor speed parameter is greater than 2, it is determined that the UAV meets the preset third parameter condition of abnormal landing matching, and the UAV belongs to the abnormal landing.
  • the ground-off telemetry data obtained for the last sortie the ground-off telemetry data whose roll angle parameter is greater than the third roll angle threshold.
  • the ground-off telemetry data whose pitch angle parameter is greater than the third pitch angle threshold the ground-off telemetry data whose pitch angle parameter is greater than the third pitch angle threshold.
  • the sequence of the motors if there are ground-off telemetry data that the first half of the motor speed parameters are all greater than the second half of the motor speed parameters or the first half of the motor speed parameters are all less than the second half of the motor speed parameters (the motor speed and power are proportional,
  • the meaning of this situation is that the power of the left propeller of the drone is larger or smaller than the power of the right propeller, so the unbalanced force will be turned over), and the maximum value of the motor speed parameter on the larger side of the data and the smaller side of the data
  • the minimum value of the motor speed parameter difference is greater than 30
  • the number of ground-off telemetry data is greater than or equal to 20, and it is determined that the drone is an abnormal fall to the ground.
  • the ground-off telemetry data with the last motor speed parameter of the last flight that is not 0. If the ground-off telemetry data corresponds to multiple motor speed parameters greater than 25, and there is a roll angle parameter greater than the first in the end data of the current flight If the second roll angle threshold or pitch angle parameter is greater than the ground-off telemetry data of the second pitch angle threshold, it is determined that the drone is an abnormal fall to the ground. If the multiple motor speed parameters corresponding to the ground-off telemetry data are greater than 90, it is determined that the UAV falls abnormally to the ground.
  • the ground-off telemetry data of the threshold, and the ground-off telemetry data with the pitch angle parameter less than the second pitch angle threshold is obtained.
  • ground-off telemetry data whose absolute value of the pitch angle parameter is greater than the second pitch angle threshold in the sorties and not all ground-off telemetry data whose absolute value of the pitch angle parameter is greater than the second pitch angle threshold, continue to perform subsequent judgments in this step; otherwise, , Continue to judge according to other judgment conditions.
  • line is the 55th position.
  • the average value of the pitch angle parameter of dataA is within 20°, and the absolute value of each corresponding pitch angle parameter is less than the third pitch angle threshold, it is determined whether the minimum value of the pitch angle parameter corresponding to dataB is greater than the third pitch angle threshold. If the minimum value of the pitch angle parameter corresponding to dataB is greater than the third pitch angle threshold, it is determined that the UAV falls abnormally to the ground. Otherwise, continue to judge according to other judgment conditions.
  • the ground-off telemetry data whose absolute value of the roll angle parameter is greater than the second roll angle threshold is obtained. If there are ground-off telemetry data whose absolute value of the roll angle parameter is greater than the second roll angle threshold in the sorties, and not all the ground-off telemetry data whose absolute value of the roll angle parameter is greater than the second roll angle threshold, continue with the subsequent judgments of this step; otherwise, , Continue to judge according to other judgment conditions. Continue to judge based on the ground-off telemetry data whose absolute value of the roll angle parameter is greater than the second roll angle threshold in the sorties.
  • the average value of the roll angle parameter of dataC is within 20°, and the absolute value of each corresponding roll angle parameter is less than the third roll angle threshold, it is determined whether the minimum value of the roll angle parameter corresponding to dataD is greater than the third roll angle threshold. If the minimum value of the roll angle parameter corresponding to dataD is greater than the third roll angle threshold, it is determined that the UAV meets the preset third parameter condition of abnormal landing match, and the UAV belongs to the abnormal landing. Otherwise, continue to judge according to other judgment conditions.
  • This embodiment provides a method for detecting abnormal landing of a UAV. According to the roll angle parameter, the pitch angle parameter and the motor speed parameter corresponding to the ground-off telemetry data, it is determined whether the UAV belongs to the abnormal landing.
  • the remote measurement data from the ground more accurately judges whether the UAV has fallen to the ground abnormally, and provides a more uniform judgment standard, which avoids human misjudgment and improves judgment efficiency.
  • FIG. 4 is a structural block diagram of a detection device for an abnormal landing of a drone provided by the fourth embodiment of the application. As shown in FIG. 4, the device includes: a data acquisition module 401, a data extraction module 402, and a drone detection module 403.
  • the data acquisition module 401 is used to acquire the telemetry data of the drone to be detected; the data extraction module 402 is used to extract effective ground-off telemetry data from the telemetry data; the drone detection module 403 , Used to detect whether the UAV belongs to the abnormal landing based on the at least one attitude related parameter determined by the ground-off telemetry data, the parameter value of the at least one component related parameter, and the preset parameter conditions for abnormal landing.
  • the detection device for abnormal landing of a drone obtains telemetry data with the drone to be detected, and extracts effective ground-off telemetry data from the telemetry data, and then according to the ground-off telemetry data Determined at least one attitude-related parameter, at least one component-related parameter parameter value, and preset parameter conditions for abnormally falling to the ground, to detect whether the drone is abnormally falling to the ground, and to solve the inconsistent judgment standard , Can not give an accurate judgment, and the judgment efficiency is too low. According to the remote measurement data of the drone, it can be more accurately judged whether the drone has abnormally fallen to the ground, and a more unified judgment standard is given to avoid human error. To improve the efficiency of judgment.
  • the attitude-related parameters may include: roll angle parameters and pitch angle parameters; the component-related parameters may be motor speed parameters; the data extraction module 402 may include: a first judgment unit for judging telemetry data Whether the satellite quantity parameter, roll angle parameter, and pitch angle parameter are included in the data; the ground-off data acquisition unit is used to obtain the ground-off telemetry in the telemetry data when determining that the telemetry data contains the satellite quantity parameter, the roll angle parameter, and the pitch angle parameter Data; the second judging unit is used to judge whether the ground-off telemetry data meets the data validity conditions; the valid data determining unit is used to determine the ground-off telemetry data as valid when the ground-off telemetry data meets the data validity conditions Telemetry data.
  • the ground clearance data acquisition unit may include: a data interception subunit for intercepting ground clearance telemetry data in the telemetry data according to the ground clearance flag in the flight status parameter corresponding to the telemetry data.
  • the second judging unit may include: a first judging subunit for judging that the number of satellites in the ground-off telemetry data is greater than the preset number of satellites according to the satellite quantity parameter corresponding to the ground-off telemetry data Whether the ratio of the fifth amount of ground-off telemetry data to the total amount of ground-off telemetry data is greater than the preset ratio threshold; the second judging subunit is used to determine whether the ratio is greater than the preset ratio threshold, according to the corresponding ground telemetry data Determine whether the drone is a simulator based on the battery voltage parameters; the third judging subunit is used to determine whether the drone has been dismantled based on the motor speed parameters corresponding to the ground telemetry data when it is determined that the drone is not a simulator Paddle; The fourth judgment subunit is used to determine that the ground-off telemetry data meets the data validity condition when it is determined that the drone has not been dismantled.
  • the drone detection module 403 may include: a first detection unit, configured to determine whether the drone meets the preset abnormal landing match according to the roll angle parameter corresponding to the ground-off telemetry data The first parameter condition; the second detection unit, when it is determined that the drone does not meet the preset first parameter condition for abnormal landing matching, according to the pitch angle parameter corresponding to the ground-off telemetry data, judge whether the drone meets the expected Set the second parameter condition for abnormal landing matching; the third detection unit is used to determine that the drone does not meet the preset second parameter condition for abnormal landing matching, for each sortie, according to the ground-off telemetry in the sortie The roll angle parameter, pitch angle parameter, and motor speed parameter corresponding to the data determine whether the UAV meets the preset third parameter condition for abnormal landing matching.
  • the first detection unit may include: a first acquisition subunit for acquiring first ground-off telemetry data whose roll angle parameter is greater than the first roll-angle threshold in the ground-off telemetry data; second The acquisition subunit is used to acquire the second ground clearance telemetry data whose roll angle parameter is less than the second roll angle threshold in the ground clearance telemetry data; the first determination subunit is used to determine that the data number of the first ground clearance telemetry data is equal to 0, or when the number of data of the second ground-off telemetry data is equal to 0, it is determined that the drone does not meet the preset first parameter condition of abnormal ground-falling matching.
  • the first detection unit may include: a third acquisition subunit, configured to acquire third ground-off telemetry data whose pitch angle parameter is greater than the first pitch-angle threshold in the ground-off telemetry data; and fourth The acquisition sub-unit is used to acquire the fourth ground-off telemetry data whose pitch angle parameter is less than the second pitch angle threshold in the ground-off telemetry data; the second determination sub-unit is used to determine that the number of the third ground-off telemetry data is equal to 0, or when the data number of the fourth ground-off telemetry data is equal to 0, it is determined that the drone does not meet the preset second parameter condition of abnormal ground-fall matching.
  • the device for detecting abnormal landing of a drone provided by the embodiments of the present application can execute the method for detecting abnormal landing of a drone provided by any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.
  • FIG. 5 is a schematic structural diagram of a computer device provided in Embodiment 5 of this application.
  • the computer equipment includes a processor 501, a memory 502, an input device 503, and an output device 504.
  • the number of processors 501 in the computer equipment may be one or more.
  • one processor 501 is taken as an example; the processor 501, memory 502, input device 503, and output device 504 in the computer equipment may use a bus or other means. Connection, Figure 5 takes the bus connection as an example.
  • the memory 502 can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for detecting abnormal landing of a drone in the embodiment of the present application (for example, unmanned
  • the processor 501 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 502, that is, realizes the above-mentioned no-fly control method for drones.
  • the memory 502 may mainly include a program storage area and a data storage area.
  • the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the terminal, etc.
  • the memory 502 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 502 may include a memory remotely provided with respect to the processor 501, and these remote memories may be connected to a computer device through a network. Examples of the above-mentioned network may include the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the input device 503 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the computer equipment.
  • the output device 504 may include a voice output device.
  • the sixth embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored.
  • the method for detecting abnormal landing of a drone provided by the embodiment of the present application is implemented, and the method includes : Obtain the telemetry data of the unmanned aerial vehicle to be detected; extract valid ground-off telemetry data from the telemetry data; according to at least one attitude related parameter determined by the ground-off telemetry data, at least one component related parameter parameter value, And the preset parameter conditions for abnormal landing to detect whether the UAV belongs to abnormal landing.
  • the computer storage media in the embodiments of the present application may adopt any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the above.
  • computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory Erasable programmable read-only memory
  • CD-ROM compact disk read-only memory
  • the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and computer-readable program code is carried therein. This propagated data signal can take many forms, including electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
  • the program code contained on the computer-readable medium can be transmitted by any suitable medium, which may include wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
  • the computer program code used to perform the operations of the present application can be written in one or more programming languages or a combination thereof.
  • the programming languages include object-oriented programming languages-such as Java, Smalltalk, C++, and also conventional Procedural programming language-such as "C" language or similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
  • the remote computer can be connected to the user’s computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider for example, using an Internet service provider to pass Internet connection.

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Abstract

一种无人机异常坠地的检测方法、装置、设备以及存储介质，该方法包括：获取待检测的无人机的遥测数据（101）；在遥测数据中，提取有效的离地遥测数据（102）；根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地（103）。

Description

无人机异常坠地的检测方法、装置、设备以及存储介质
本公开要求在2019年04月26日提交中国专利局、申请号为201910344039.8的中国专利申请的优先权，以上申请的全部内容通过引用结合在本公开中。
技术领域
本申请实施例涉及无人机技术领域，例如涉及一种无人机异常坠地的检测方法、装置、设备以及存储介质。
背景技术
随着无人机技术的日渐成熟，无人机逐渐用于农业、林业、航拍、测绘以及巡检等领域。普通用户在使用无人机时，由于操作不当或者无人机自身故障，会出现炸机现象。炸机是指由于操作不当或机器故障等因素导致的无人机异常坠地。
异常坠地的无人机完全报废，难以再次使用，给用户造成较大损失。厂家为了减轻用户负担，通常会推出补偿措施以激励用户使用无人机。例如，当判断无人机由于异常坠地而无法使用时，用户可选择将异常坠地后的无人机退回厂家。厂家收回无人机，并以低于市价的价格将同种型号的无人机卖给用户，以减轻用户损失，或者返给用户一些维修基金。维修基金用于维修或者购买新机。
相关技术中，只能通过人为目测来判断无人机是否属于异常坠地，而相关技术的缺陷在于，采用人为目测的方式判断无人机是否属于异常坠地，容易出现较大误差，参考价值有限，无人机异常坠地判断标准不统一，不能给出准确的判断。而且采用人工判断无人机是否属于异常坠地，需要大量的人工投入，所花费时间较长，判断效率太低。
发明内容
本申请实施例提供一种无人机异常坠地的检测方法、装置、设备以及存储介质，以优化相关技术中的无人机异常坠地的检测方法，准确地评判无人机是否发生炸机，避免人为误判，提高判断效率。
第一方面，本申请实施例提供了一种无人机异常坠地的检测方法，包括：
获取待检测的无人机的遥测数据；
在遥测数据中，提取有效的离地遥测数据；
根据预设的异常坠地匹配的参数条件，以及由离地遥测数据确定的姿态关联参数的参数值和部件关联参数的参数值，检测无人机是否属于异常坠地。
第二方面，本申请实施例还提供了一种无人机异常坠地的检测方装置，包括：
数据获取模块，被配置为获取待检测的无人机的遥测数据；
数据提取模块，被配置为在遥测数据中，提取有效的离地遥测数据；
无人机检测模块，被配置为根据预设的异常坠地匹配的参数条件，以及由离地遥测数据确定的姿态关联参数的参数值和部件关联参数的参数值，检测无人机是否属于异常坠地。
第三方面，本申请实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时实现本申请任一实施例所提供的无人机异常坠地的检测方法。
第四方面，本申请实施例还提供了一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时实现本申请任一实施例所提供的无人机异常坠地的检测方法。
附图说明
图1为本申请实施例一提供的一种无人机异常坠地的检测方法的流程图；
图2为本申请实施例二提供的一种无人机异常坠地的检测方法的流程图；
图3为本申请实施例三提供的一种无人机异常坠地的检测方法的流程图；
图4为本申请实施例四提供的一种无人机异常坠地的检测装置的结构框图；
图5为本申请实施例五提供的一种计算机设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本申请，而非对本申请的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本申请相关的部分而非全部结构。
实施例一
图1为本申请实施例一提供的一种无人机异常坠地的检测方法的流程图， 本实施例可适用于的情况，该方法可以由无人机异常坠地的检测装置来执行，所述装置由软件和/或硬件来执行，并一般可集成在计算机设备中，例如，服务器或者终端设备。如图1所示，该方法可以包括如下步骤：
步骤101、获取待检测的无人机的遥测数据。
在一些实施例中，遥测数据是通过无人机的传感器模块采集，被服务器接收到的飞行数据，来自无人机，可以反映无人机的数字特征或状态。
无人机可以包括传感器模块、数据记录装置及飞行控制模块。
无人机的传感器模块用于采集无人机的飞行数据。传感器模块可以包括三轴加速度计、三轴陀螺仪、磁罗盘、气压计、全球定位系统(Global Positioning System，GPS)、电压检测单元和电流检测单元。飞行数据可以包括三轴姿态角、角速率、加速度、位置、无人机飞行时的卫星状态数据、以及部件的电压和电流等。
传感器模块将所采集到的飞行数据通过本地总线传送至飞行控制模块，飞行控制模块接收传感器模块所采集的飞行数据以及来自用户的控制命令，进行处理融合后生成飞行指令并发送给执行机构，控制无人机飞行，同时将所接收的飞行数据以及控制指令发送给数据记录装置。
数据记录装置用于存储用户控制指令以及传感器模块的飞行数据，将用户控制指令以及传感器模块的飞行数据发送给服务器。数据记录装置可以包括主板以及集成于主板上的处理器、存储模块和无线通信模块，处理器与飞行控制模块连接，存储模块和无线通信模块分别与处理器连接，无线通信模块与服务器通信。
由此，可以实现通过服务器对无人机的遥测数据进行保存，为后续对无人机飞行状态的分析提供依据。
待检测的无人机可以是需要判断是否属于异常坠地的无人机。获取待检测的无人机在设定检测时间区间内的每个时刻的遥测数据，即获取待检测的无人机在每个时刻的三轴姿态角、角速率、加速度、位置、无人机飞行时的卫星状态数据、以及部件的电压和电流等。
每架无人机的数据都可以记录成对应的多个文件。每个文件的内容可以根据记录方式有所区别。可选的，记录方式为按照上电来记录。例如，无人机在一次通电过程中的数据记录为一个文件。该文件中可以包括无人机在一次通电过程中的遥测数据。可选的，记录方式为按照起落的架次来记录。该文件中可 以包括无人机一个架次中的遥测数据。例如，将无人机在一个架次中的数据记录为一个文件。无人机解锁和加锁的过程为一个架次。无人机的飞行状态参数中的加解锁状态标志，表明无人机是加锁还是解锁。根据加解锁状态标志的数值，可以检测无人机是加锁还是解锁，并在检测到无人机加锁时，将获取的无人机的数据记录为一个文件，后续获取的数据将记录在新的文件之中。
服务器在获取待检测的无人机对应的文件之后，可以在文件中获取待检测的无人机的遥测数据，并根据无人机的遥测数据较为准确地评判无人机是否发生异常坠地。
步骤102、在遥测数据中，提取有效的离地遥测数据。
在一些实施例中，获取待检测的无人机在设定检测时间区间内的遥测数据后，可以先检测遥测数据的数据完整性。在确定遥测数据完整时，获取遥测数据中的离地遥测数据。如果遥测数据不完整，则可以不再根据遥感数据判断待检测的无人机是否属于异常坠地，直接确定待检测的无人机不属于异常坠地。
可选的，检测遥测数据的数据完整性的步骤，包括：判断遥测数据中是否包含卫星数量参数、滚转角参数、俯仰角参数以及电机转速参数。
卫星数量参数是无人机飞行时的卫星数量。在判断待检测的无人机是否属于异常坠地的无人机的过程中，可以校验待检测的无人机飞行时的卫星状态。例如，校验待检测的无人机飞行时的卫星状态的步骤可以包括：根据卫星数量参数确定待检测的无人机飞行时的卫星数量，然后根据卫星数量确定待检测的无人机是室内飞行或者室外飞行。滚转角参数是无人机的滚转角。俯仰角参数是无人机的俯仰角。电机转速参数为无人机的每个电机的电机转速。如果遥测数据中缺少卫星数量参数、滚转角参数、俯仰角参数以及电机转速参数中的任意一种参数，则确定该遥测数据不完整。
在一些实施例中，离地遥测数据是指待检测的无人机起飞离地的遥测数据。离地数据的获取方法可以为：根据遥测数据对应的飞行状态参数中的离地标志，截取遥测数据中的离地遥测数据。如果遥测数据中不存在离地遥测数据，则可以直接确定待检测的无人机不属于异常坠地。如果遥测数据中存在离地遥测数据，则截取遥测数据中的离地遥测数据，判断离地遥测数据是否满足数据有效条件。在确定离地遥测数据满足数据有效条件时，将离地遥测数据确定为有效的离地遥测数据。如果离地遥测数据不满足数据有效条件，则可以不再根据离地遥感数据判断待检测的无人机是否属于异常坠地，直接确定待检测的无人机 不属于异常坠地。
可选的，判断离地遥测数据是否满足数据有效条件的步骤，可以包括：根据离地遥测数据对应的卫星数量参数，判断离地遥测数据中卫星数量大于预设卫星数量的第五离地遥测数据的数量与离地遥测数据的数据总量的比值是否大于预设比例门限；在确定比值大于预设比例门限时，根据离地遥测数据对应的电池电压参数，判断无人机是否是模拟器；在确定无人机不是模拟器时，根据离地遥测数据对应的电机转速参数，判断无人机是否被拆桨(即被拆螺旋桨)；在确定无人机没有被拆桨时，确定离地遥测数据满足数据有效条件。
步骤103、根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地。
可选的，姿态关联参数可以包括：滚转角参数和俯仰角参数。滚转角参数和俯仰角参数是无人机的滚转角和俯仰角。部件关联参数可以为电机转速参数。电机转速参数为无人机的每个电机的电机转速。
可选的，根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地的步骤，包括：根据离地遥测数据对应的滚转角参数，判断无人机是否满足预设的异常坠地匹配的第一参数条件；在确定无人机不满足预设的异常坠地匹配的第一参数条件时，根据离地遥测数据对应的俯仰角参数，判断无人机是否满足预设的异常坠地匹配的第二参数条件；在确定无人机不满足预设的异常坠地匹配的第二参数条件时，根据离地遥测数据对应的架次参数，针对每一个架次，根据架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断无人机是否满足预设的异常坠地匹配的第三参数条件。由此，依次根据滚转角参数、俯仰角参数、电机转速参数判断无人机是否属于异常坠地。
本实施例提供的一种无人机异常坠地的检测方法，通过获取与待检测的无人机的遥测数据，并在遥测数据中，提取有效的离地遥测数据，然后根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地，解决了无人机异常坠地判断标准不统一，不能给出准确的判断，而且判断效率太低的问题，可以根据无人机的遥测数据较为准确地评判无人机是否发生异常坠地， 给出了一个较为统一的判断标准，避免了人为误判，提高判断效率。
实施例二
图2为本申请实施例二提供的一种无人机异常坠地的检测方法的流程图。本实施例可以与上述一个或者多个实施例中每个可选方案结合，在本实施例中，姿态关联参数包括：滚转角参数和俯仰角参数；部件关联参数为电机转速参数。
在遥测数据中，提取有效的离地遥测数据的步骤，可以包括：判断遥测数据中是否包含卫星数量参数、滚转角参数以及俯仰角参数；在确定遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数时，获取遥测数据中的离地遥测数据；判断离地遥测数据是否满足数据有效条件；在确定离地遥测数据满足数据有效条件时，将离地遥测数据确定为有效的离地遥测数据。
如图2所示，该方法可以包括如下步骤：
步骤201、获取待检测的无人机的遥测数据。
步骤202、判断遥测数据中是否包含卫星数量参数、滚转角参数以及俯仰角参数。
在一些实施例中，如果遥测数据中缺少卫星数量参数、滚转角参数以及俯仰角参数中的任意一种参数，则可以确定遥测数据不完整，不再根据遥感数据判断待检测的无人机是否属于异常坠地的无人机，直接确定待检测的无人机不属于异常坠地的无人机。
步骤203、在确定遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数时，获取遥测数据中的离地遥测数据。
在一些实施例中，如果遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数，则可以根据遥测数据对应的飞行状态参数中的离地标志，截取遥测数据中的离地遥测数据。在一些实施例中，如果遥测数据对应的飞行状态参数中的离地标志为0，表示该无人机未起飞离地，该遥测数据不是离地遥测数据；如果遥测数据对应的飞行状态参数中的离地标志不为0，表示该无人机起飞离地，该遥测数据是离地遥测数据。如果遥测数据中存在飞行状态参数中的离地标志不为0的遥测数据，则截取飞行状态参数中的离地标志不为0的遥测数据，作为遥测数据中的离地遥测数据。如果遥测数据中不存在离地遥测数据，则可以直接确定待检测的无人机不属于异常坠地的无人机。
步骤204、判断离地遥测数据是否满足数据有效条件。
在一些实施例中，先根据离地遥测数据对应的卫星数量参数，判断离地遥 测数据中卫星数量大于预设卫星数量的第五离地遥测数据的数量与离地遥测数据的数据总量的比值是否大于预设比例门限。
预设卫星数量和预设比例门限可以根据业务需求进行设置。如果确定比值大于预设比例门限，则继续根据离地遥测数据对应的电池电压参数进行数据有效性的判断；如果确定比值小于等于预设比例门限，则确定离地遥测数据不满足数据有效条件。
在一个具体实例中，预设卫星数量为4，预设比例门限为0.7。根据每个离地遥测数据对应的卫星数量参数，确定卫星数量大于4的离地遥测数据的数据数量。计算卫星数量大于4的离地遥测数据的数据数量与离地遥测数据的数据总量的比值。如果确定比值大于0.7，则继续根据离地遥测数据对应的电池电压参数进行数据有效性的判断；如果确定比值小于等于0.7，则确定离地遥测数据不满足数据有效条件。比值大于0.7，表明无人机在室内；比值小于等于0.7，表明无人机在室外。
在确定比值大于预设比例门限时，可以根据离地遥测数据对应的电池电压参数，判断无人机是否是模拟器。电池电压参数是无人机电池电压，单位为V。如果每个离地遥测数据对应的电池电压参数全为0，或者每个离地遥测数据对应的电池电压参数为同一数值，即无人机电池电压始终保持不变，则确定无人机是模拟器，确定离地遥测数据不满足数据有效条件。如果为其他情况，则可以确定无人机不是模拟器，继续根据离地遥测数据对应的电机转速参数进行数据有效性的判断。
在确定无人机不是模拟器时，可以根据离地遥测数据对应的电机转速参数，判断无人机是否被拆桨。电机转速参数是无人机电机的电机转速。无人机最多可以有8个电机。无人机可以有4个电机，6个电机或者8个电机。例如，无人机有8个电机。在离地遥测数据中，有与每个电机对应的电机转速参数，即每个时刻的离地遥测数据中有8个电机转速参数。
在一些实施例中，获取每个离地遥测数据对应的电机转速参数最小值，作为最小值组合。电机转速参数最小值是离地遥测数据所包括的多个电机转速参数中的最小的电机转速参数。在最小值组合中，筛选出数值大于0的电机转速参数，将数值大于0的电机转速参数的数据个数记为sum。在数值大于0的电机转速参数中筛选出数值小于等于12的数据，将数值小于等于12的数据个数记为num。如果num/sum大于0.12，则确定无人机被拆桨，确定离地遥测数据不 满足数据有效条件；如果num/sum小于等于0.12，则确定无人机没有被拆桨，确定离地遥测数据满足数据有效条件。12和0.12都是可调节的参数，可以根据业务需求进行调节。
步骤205、在确定离地遥测数据满足数据有效条件时，将离地遥测数据确定为有效的离地遥测数据。
步骤206、根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地。
本实施例提供的一种无人机异常坠地的检测方法，通过在确定遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数时，获取遥测数据中的离地遥测数据，并判断离地遥测数据是否满足数据有效条件，在确定离地遥测数据满足数据有效条件时，将离地遥测数据确定为有效的离地遥测数据，可以根据遥测数据对应的参数，在遥测数据中，提取有效的离地遥测数据。
实施例三
图3为本申请实施例三提供的一种无人机异常坠地的检测方法的流程图。本实施例可以与上述一个或者多个实施例中每个可选方案结合，在本实施例中，根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地的步骤，可以包括：根据离地遥测数据对应的滚转角参数，判断无人机是否满足预设的异常坠地匹配的第一参数条件；在确定无人机不满足预设的异常坠地匹配的第一参数条件时，根据离地遥测数据对应的俯仰角参数，判断无人机是否满足预设的异常坠地匹配的第二参数条件；在确定无人机不满足预设的异常坠地匹配的第二参数条件时，针对每一个架次，根据架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断无人机是否满足预设的异常坠地匹配的第三参数条件。
如图3所示，该方法可以包括如下步骤：
步骤301、获取待检测的无人机的遥测数据。
步骤302、在遥测数据中，提取有效的离地遥测数据。
步骤303、根据离地遥测数据对应的滚转角参数，判断无人机是否满足预设的异常坠地匹配的第一参数条件。
在一些实施例中，获取离地遥测数据中滚转角参数大于第一滚转角门限的 第一离地遥测数据。即第一离地遥测数据为滚转角参数大于第一滚转角门限的离地遥测数据。获取离地遥测数据中滚转角参数小于第二滚转角门限的第二离地遥测数据。即第二离地遥测数据为滚转角参数大于第二滚转角门限的离地遥测数据。在确定第一离地遥测数据的数据个数等于0，或者第二离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第一参数条件。
可选的，第一滚转角门限为90°(90°为可调参数)。第二滚转角门限为50°(50°为可调参数)。获取离地遥测数据中滚转角参数大于90°的第一离地遥测数据。获取离地遥测数据中滚转角参数小于50°的第二离地遥测数据。在确定第一离地遥测数据的数据个数等于0，或者第二离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第一参数条件。
在确定第一离地遥测数据的数据个数大于0，且第二离地遥测数据的数据个数大于0时，依次检查第一离地遥测数据中的每一个离地遥测数据的滚转角参数，以及该离地遥测数据在全部遥测数据中所处的排序位置。全部遥测数据按照时间先后顺序排序。判断第一离地遥测数据中的每一个离地遥测数据是否属于全部遥测数据中的最后3个遥测数据。
如果第一离地遥测数据中的任意一个离地遥测数据属于全部遥测数据中的最后3个遥测数据，则确定无人机满足预设的异常坠地匹配的第一参数条件，无人机属于异常坠地。3是可调参数。第一离地遥测数据中的任意一个离地遥测数据属于全部遥测数据中的最后3个遥测数据，表示无人机异常坠地之后，数据传输中断，导致后面的数据缺少。即全部遥测数据中的最后3个遥测数据中出现了滚转角参数大于90°的遥测数据，极大可能是要侧翻的，结合后续数据中断，判定无人机实际侧翻了。
如果第一离地遥测数据中的离地遥测数据都不属于全部遥测数据中的最后3个遥测数据，则继续在全部遥测数据中，检查位于该离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据(6为可调参数)。判断位于离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据中是否存在滚转角参数大于第三滚转角门限的遥测数据。可选的，第三滚转角门限为40°(40°为可调参数)。
如果位于第一离地遥测数据中的任意一个离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据中，存在滚转角参数大于第三滚转角门限的遥测数据，则确定无人机满足预设的异常坠地匹配的第一参数条件，无人机属 于异常坠地。
如果位于第一离地遥测数据中的任意一个离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据中，都不存在滚转角参数大于第三滚转角门限的遥测数据，则确定无人机不满足预设的异常坠地匹配的第一参数条件。
步骤304、在确定无人机不满足预设的异常坠地匹配的第一参数条件时，根据离地遥测数据对应的俯仰角参数，判断无人机是否满足预设的异常坠地匹配的第二参数条件。
在一些实施例中，获取离地遥测数据中俯仰角参数大于第一俯仰角门限的第三离地遥测数据，即第三离地遥测数据为俯仰角参数大于第一俯仰角门限的离地遥测数据。获取离地遥测数据中俯仰角参数小于第二俯仰角门限的第四离地遥测数据，即第四离地遥测数据为俯仰角参数大于第二俯仰角门限的离地遥测数据。在确定第三离地遥测数据的数据个数等于0，或者第四离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第二参数条件。
可选的，第一俯仰角门限为90°(90°为可调参数)。第二俯仰角门限为50°(50°为可调参数)。获取离地遥测数据中俯仰角参数大于90°的第三离地遥测数据。获取离地遥测数据中俯仰角参数小于50°的第四离地遥测数据。在确定第三离地遥测数据的数据个数等于0，或者第四离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第二参数条件。
在确定第三离地遥测数据的数据个数大于0，且第四离地遥测数据的数据个数大于0时，依次检查第三离地遥测数据中的每一个离地遥测数据的俯仰角参数，以及该离地遥测数据在全部遥测数据中所处的排序位置。全部遥测数据按照时间先后顺序排序。判断第三离地遥测数据中的每一个离地遥测数据是否属于全部遥测数据中的最后3个遥测数据。
如果第三离地遥测数据中的任意一个离地遥测数据属于全部遥测数据中的最后3个遥测数据，则确定无人机满足预设的异常坠地匹配的第二参数条件，无人机属于异常坠地。3是可调参数。第三离地遥测数据中的任意一个离地遥测数据属于全部遥测数据中的最后3个遥测数据，表示无人机异常坠地之后，数据传输中断，导致后面的数据缺少。
如果第三离地遥测数据中的离地遥测数据都不属于全部遥测数据中的最后3个遥测数据，则继续在全部遥测数据中，检查位于该离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据(6为可调参数)。判断位于离地遥测数 据所处的排序位置的前6个遥测数据和后6个遥测数据中是否存在俯仰角参数大于第三俯仰角门限的遥测数据。可选的，第三俯仰角门限为40°(40°为可调参数)。
如果位于第三离地遥测数据中的任意一个离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据中，存在俯仰角参数大于第三俯仰角门限的遥测数据，则确定无人机满足预设的异常坠地匹配的第二参数条件，无人机属于异常坠地。
如果位于第三离地遥测数据中的任意一个离地遥测数据所处的排序位置的前6个遥测数据和后6个遥测数据中，都不存在俯仰角参数大于第三俯仰角门限的遥测数据，则确定无人机不属于满足预设的异常坠地匹配的第二参数条件。
步骤305、在确定无人机不满足预设的异常坠地匹配的第二参数条件时，针对每一个架次，根据架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断无人机是否满足预设的异常坠地匹配的第三参数条件。
在一些实施例中，无人机解锁起飞到落地加锁的一个过程叫做一个架次。无人机的飞行状态参数中的加解锁状态标志，表明无人机是加锁还是解锁。根据加解锁状态标志的数值，可以检测无人机是加锁还是解锁。根据加解锁状态标志的数值，可以从全量的数据中抽出每个架次的数据，直接就是针对每个架次的离地遥测数据进行进一步的分析，根据架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断无人机是否满足预设的异常坠地匹配的第三参数条件。
每一个架次的离地遥测数据是按照时间先后顺序排序的，去掉每一个架次的前15个离地遥测数据，将剩余的离地遥测数据作为每一个架次的架次数据。15为可调参数。去掉每一个架次的前15个离地遥测数据，是为了去掉起飞开始电机转速不稳定的离地遥测数据。
如果当前架次的架次数据的数据个数小于2，则认为架次数据的数据量太小，不进行判断，继续判断下一个架次。如果当前架次的架次数据的数据个数大于2，则继续下面的判断。
获取当前架次的离地遥测数据中，滚转角参数大于第二滚转角门限的离地遥测数据，记为r。获取当前架次的离地遥测数据中，俯仰角参数大于第二俯仰角门限的离地遥测数据，记为pitch。r和pitch为疑似可能侧翻的离地遥测数据。
如果r的数据个数大于0，或者pitch的数据个数大于0，并且当前架次的离 地遥测数据中存在滚转角参数小于第二滚转角门限的离地遥测数据，以及俯仰角参数小于第二俯仰角门限的离地遥测数据，则进行后续的判断。
获取当前架次的每个离地遥测数据对应的电机转速参数最大值，作为最大值组合。最大值组合中的电机转速参数按照时间先后顺序排序。对最大值组合做聚类算法，将最大值组合中的电机转速参数分为2大类(将最大值组合中的电机转速参数按照大小划分为2个分类，每个分类的数值是比较接近的)，得到第一分类和第二分类，并求得第一分类的中心点V1，以及第二分类的中心点V2。中心点即为对应分类中数据的平均值。
如果V1和V2都大于90，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。V1和V2都大于90代表电机转速一直都很大，正常飞行是不存在这种的，是无人机起飞没多久就异常坠地的情况。
如果V1和V2中有一个值小于20，同时另外一个值大于90，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。V1和V2中有一个值小于20，同时另外一个值大于90，代表无人机起飞没多久就异常坠地的情况。一开始无人机最大电机转速均值小于20，之后异常坠地，最大电机转速均值大于90。
如果V1和V2都大于25，且两者差值大于20(25和20为可调参数)，则依次检查数值较大的分类中的电机转速参数(即异常数据)在最大值组合中所处的排序位置。如果异常数据位于最大值组合的前半段，跳过；如果异常数据的排序位置和上一个异常数据的排序位置相差15位，跳过；如果一个异常数据位于最大值组合的后半段，同时该异常数据的排序位置和上一个异常数据的排序位置相差15位以内，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。15为可调参数。判断相差15位主要是为了屏蔽突变的干扰数据，干扰数据往往是一条的，而异常坠地的异常数据往往是扎堆的。正常飞行中，一般电机转速参数的差值不会相差那么大，存在这么大的差值，说明可能有异常。数值大的电机转速参数可能是异常数据，异常坠地时异常的数据比正常数据少，即数值较大的分类中的电机转速参数的数据个数可能比较少。
在V1和V2不符合上述所有条件时，获取当前架次的末尾数据。末尾数据是当前架次的最后30个离地遥测数据。30为可调参数。获取当前架次的末尾数据对应的电机转速参数最大值，记做max。如果max大于等于90，并且max和V1的差值、max和V2的差值都大于10，并且末尾数据中的每个离地遥测数据 对应的电机转速参数最大值中，数值大于max-5的电机转速参数最大值的数据个数大于2个，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。在电机转速很小，或者电机转速差值不大很匀称的时候，考察当前架次的末尾数据。如果末尾数据中，存在电机转速大于等于90的离地遥测数据，并且这个数值不是只出现一次的突变值，那么认为无人机属于异常坠地。
如果当前架次是最后一个架次，除了执行上述检查外，还可以进行进一步检查。
获取最后一个架次的离地遥测数据中，滚转角参数大于第三滚转角门限的离地遥测数据。获取最后一个架次的离地遥测数据中，俯仰角参数大于第三俯仰角门限的离地遥测数据。检查所获取的每一个离地遥测数据对应的多个电机转速参数。按照电机顺序，如果存在前一半的电机转速参数全大于后一半的电机转速参数或者前一半的电机转速参数全小于后一半的电机转速参数的离地遥测数据(电机转速和动力是成比例的，这种情况的含义是无人机左边桨的动力比右边桨的动力大或者小，那么受力不平衡要翻掉的)，而且数据较大一边的电机转速参数最大值和数据较小一边的电机转速参数最小值相差大于30的离地遥测数据的数据个数大于等于20，则确定无人机属于异常坠地。
获取最后一个架次的末尾数据。检查末尾数据中的每一个离地遥测数据对应的多个电机转速参数。按照电机顺序，如果存在前一半的电机转速参数全大于后一半的电机转速参数或者前一半的电机转速参数全小于后一半的电机转速参数的离地遥测数据，而且存在数据较大一边的电机转速参数最大值和数据较小一边的电机转速参数最小值相差大于30的离地遥测数据，则确定无人机属于异常坠地。
获取最后一个架次的最后一个电机转速参数不为0的离地遥测数据，如果该离地遥测数据对应的多个电机转速参数有大于25的，而且当前架次的末尾数据中存在滚转角参数大于第二滚转角门限或者俯仰角参数大于第二俯仰角门限的离地遥测数据，则确定无人机属于异常坠地。如果该离地遥测数据对应的多个电机转速参数有大于90的，则确定无人机属于异常坠地。
针对每个架次，如果该架次的离地遥测数据不满足r的数据个数大于0，或者pitch的数据个数大于0，并且当前架次的离地遥测数据中存在滚转角参数小于第二滚转角门限的离地遥测数据，以及俯仰角参数小于第二俯仰角门限的离 地遥测数据的条件，则获取俯仰角参数绝对值大于第二俯仰角门限的离地遥测数据。如果架次中存在俯仰角参数绝对值大于第二俯仰角门限的离地遥测数据，而且不全是俯仰角参数绝对值大于第二俯仰角门限的离地遥测数据，则继续执行本步后续判断；否则，根据其他判断条件继续进行判断。根据架次中滚转角参数绝对值大于第二俯仰角门限的离地遥测数据继续进行判断。记第一个俯仰角参数绝对值大于第二俯仰角门限的离地遥测数据所在排序位置为line。获取从最后一个架次的起始排序位置开始到排序位置line-3的离地遥测数据，记为dataA。例如，line为第55位。获取从最后一个架次的第1位开始到52位的离地遥测数据，记为dataA。获取从排序位置line开始到本架次的结束排序位置的离地遥测数据，记为dataB。
如果dataA的俯仰角参数均值在20°以内，并且对应的每个俯仰角参数绝对值小于第三俯仰角门限，则判断dataB对应的俯仰角参数最小值是否大于第三俯仰角门限。如果dataB对应的俯仰角参数最小值大于第三俯仰角门限，则确定无人机属于异常坠地。否则，根据其他判断条件继续进行判断。
如果dataB的数据个数小于10，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。否则，根据其他判断条件继续进行判断。
在架次的离地遥测数据中，获取滚转角参数绝对值大于第二滚转角门限的离地遥测数据。如果架次中存在滚转角参数绝对值大于第二滚转角门限的离地遥测数据，而且不全是滚转角参数绝对值大于第二滚转角门限的离地遥测数据，则继续执行本步后续判断；否则，根据其他判断条件继续进行判断。根据架次中滚转角参数绝对值大于第二滚转角门限的离地遥测数据继续进行判断。记第一个滚转角参数绝对值大于第二滚转角门限的离地遥测数据所在排序位置为line。获取从最后一个架次的起始排序位置开始到排序位置line-3的离地遥测数据，记为dataC。例如，line为第55位。获取从最后一个架次的第1位开始到52位的离地遥测数据，记为dataC。获取从排序位置line开始到本架次的结束排序位置的离地遥测数据，记为dataD。
如果dataC的滚转角参数均值在20°以内，并且对应的每个滚转角参数绝对值小于第三滚转角门限，则判断dataD对应的滚转角参数最小值是否大于第三滚转角门限。如果dataD对应的滚转角参数最小值大于第三滚转角门限，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。否则，根据其他判断条件继续进行判断。
如果dataD的数据个数小于10，则确定无人机满足预设的异常坠地匹配的第三参数条件，无人机属于异常坠地。否则，根据其他判断条件继续进行判断。
如果根据上述全部判断条件进行判断后，还未确定无人机满足预设的异常坠地匹配的第三参数条件，则确定无人机不满足预设的异常坠地匹配的第三参数条件，无人机不属于异常坠地。
本实施例提供的一种无人机异常坠地的检测方法，根据离地遥测数据对应的滚转角参数、俯仰角参数以及电机转速参数依次判断无人机是否属于异常坠地，可以根据无人机的离地遥测数据较为准确地评判无人机是否发生异常坠地，给出了一个较为统一的判断标准，避免了人为误判，提高判断效率。
实施例四
图4为本申请实施例四提供的一种无人机异常坠地的检测装置的结构框图。如图4所示，所述装置包括：数据获取模块401、数据提取模块402以及无人机检测模块403。
在一些实施例中，数据获取模块401，用于获取待检测的无人机的遥测数据；数据提取模块402，用于在遥测数据中，提取有效的离地遥测数据；无人机检测模块403，用于根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地。
本实施例提供的一种无人机异常坠地的检测装置，通过获取与待检测的无人机的遥测数据，并在遥测数据中，提取有效的离地遥测数据，然后根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地，解决了无人机异常坠地判断标准不统一，不能给出准确的判断，而且判断效率太低的问题，可以根据无人机的遥测数据较为准确地评判无人机是否发生异常坠地，给出了一个较为统一的判断标准，避免了人为误判，提高判断效率。
在上述每个实施例的基础上，姿态关联参数可以包括：滚转角参数和俯仰角参数；部件关联参数可以为电机转速参数；数据提取模块402可以包括：第一判断单元，用于判断遥测数据中是否包含卫星数量参数、滚转角参数以及俯仰角参数；离地数据获取单元，用于在确定遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数时，获取遥测数据中的离地遥测数据；第二判断单元，用于判断离地遥测数据是否满足数据有效条件；有效数据确定单元，用于在确 定离地遥测数据满足数据有效条件时，将离地遥测数据确定为有效的离地遥测数据。
在上述每个实施例的基础上，离地数据获取单元可以包括：数据截取子单元，用于根据遥测数据对应的飞行状态参数中的离地标志，截取遥测数据中的离地遥测数据。
在上述每个实施例的基础上，第二判断单元可以包括：第一判断子单元，用于根据离地遥测数据对应的卫星数量参数，判断离地遥测数据中卫星数量大于预设卫星数量的第五离地遥测数据的数量与离地遥测数据的数据总量的比值是否大于预设比例门限；第二判断子单元，用于在确定比值大于预设比例门限时，根据离地遥测数据对应的电池电压参数，判断无人机是否是模拟器；第三判断子单元，用于在确定无人机不是模拟器时，根据离地遥测数据对应的电机转速参数，判断无人机是否被拆桨；第四判断子单元，用于在确定无人机没有被拆桨时，确定离地遥测数据满足数据有效条件。
在上述每个实施例的基础上，无人机检测模块403可以包括：第一检测单元，用于根据离地遥测数据对应的滚转角参数，判断无人机是否满足预设的异常坠地匹配的第一参数条件；第二检测单元，用于在确定无人机不满足预设的异常坠地匹配的第一参数条件时，根据离地遥测数据对应的俯仰角参数，判断无人机是否满足预设的异常坠地匹配的第二参数条件；第三检测单元，用于在确定无人机不满足预设的异常坠地匹配的第二参数条件时，针对每一个架次，根据架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断无人机是否满足预设的异常坠地匹配的第三参数条件。
在上述每个实施例的基础上，第一检测单元可以包括：第一获取子单元，用于获取离地遥测数据中滚转角参数大于第一滚转角门限的第一离地遥测数据；第二获取子单元，用于获取离地遥测数据中滚转角参数小于第二滚转角门限的第二离地遥测数据；第一确定子单元，用于在确定第一离地遥测数据的数据个数等于0，或者第二离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第一参数条件。
在上述每个实施例的基础上，第一检测单元可以包括：第三获取子单元，用于获取离地遥测数据中俯仰角参数大于第一俯仰角门限的第三离地遥测数据；第四获取子单元，用于获取离地遥测数据中俯仰角参数小于第二俯仰角门限的第四离地遥测数据；第二确定子单元，用于在确定第三离地遥测数据的数据个 数等于0，或者第四离地遥测数据的数据个数等于0时，确定无人机不满足预设的异常坠地匹配的第二参数条件。
本申请实施例所提供的无人机异常坠地的检测装置可执行本申请任意实施例所提供的无人机异常坠地的检测方法，具备执行方法相应的功能模块和有益效果。
实施例五
图5为本申请实施例五提供的一种计算机设备的结构示意图。如图5所示，该计算机设备包括处理器501、存储器502、输入装置503、输出装置504。计算机设备中处理器501的数量可以是一个或多个，图5中以一个处理器501为例；计算机设备中的处理器501、存储器502、输入装置503、输出装置504可以通过总线或其他方式连接，图5中以通过总线连接为例。
存储器502作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序以及模块，如本申请实施例中的无人机异常坠地的检测方法对应的程序指令/模块(例如，无人机异常坠地的检测装置中的数据获取模块401、数据提取模块402以及无人机检测模块403)。处理器501通过运行存储在存储器502中的软件程序、指令以及模块，从而执行计算机设备的各种功能应用以及数据处理，即实现上述的无人机的禁飞控制方法。
存储器502可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端的使用所创建的数据等。此外，存储器502可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器502可包括相对于处理器501远程设置的存储器，这些远程存储器可以通过网络连接至计算机设备。上述网络的实例可包括互联网、企业内部网、局域网、移动通信网及其组合。
输入装置503可用于接收输入的数字或字符信息，以及产生与计算机设备的用户设置以及功能控制有关的键信号输入。输出装置504可包括语音输出装置。
实施例六
本申请实施例六还提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现本申请实施例提供的无人机异常坠地的检测方法，该方法包括：获取待检测的无人机的遥测数据；在遥测数据中，提 取有效的离地遥测数据；根据由离地遥测数据确定的至少一项姿态关联参数，至少一项部件关联参数的参数值，以及预设的异常坠地匹配的参数条件，检测无人机是否属于异常坠地。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，可包括电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，可以包括无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。

Claims (10)

  1. 一种无人机异常坠地的检测方法，包括：
    获取待检测的无人机的遥测数据；
    在所述遥测数据中，提取有效的离地遥测数据；
    根据预设的异常坠地匹配的参数条件，以及由所述离地遥测数据确定的姿态关联参数的参数值和部件关联参数的参数值，检测所述无人机是否属于异常坠地。
  2. 根据权利要求1所述的方法，其中，所述姿态关联参数包括：滚转角参数和俯仰角参数；所述部件关联参数为电机转速参数；
    在所述遥测数据中，提取有效的离地遥测数据的步骤，包括：
    判断所述遥测数据中是否包含卫星数量参数、滚转角参数以及俯仰角参数；
    在确定所述遥测数据中包含卫星数量参数、滚转角参数以及俯仰角参数时，获取所述遥测数据中的离地遥测数据；
    判断所述离地遥测数据是否满足数据有效条件；
    在确定所述离地遥测数据满足数据有效条件时，将所述离地遥测数据确定为有效的离地遥测数据。
  3. 根据权利要求2所述的方法，其中，获取所述遥测数据中的离地遥测数据的步骤，包括：
    根据所述遥测数据对应的飞行状态参数中的离地标志，截取所述遥测数据中的离地遥测数据。
  4. 根据权利要求2所述的方法，其中，判断所述离地遥测数据是否满足数据有效条件的步骤，包括：
    根据所述离地遥测数据对应的卫星数量参数，判断所述离地遥测数据中卫星数量大于预设卫星数量的第五离地遥测数据的数量与所述离地遥测数据的数据总量的比值是否大于预设比例门限；
    在确定所述比值大于所述预设比例门限时，根据所述离地遥测数据对应的电池电压参数，判断所述无人机是否是模拟器；
    在确定所述无人机不是模拟器时，根据所述离地遥测数据对应的电机转速参数，判断所述无人机是否被拆桨；
    在确定所述无人机没有被拆桨时，确定所述离地遥测数据满足数据有效条件。
  5. 根据权利要求2所述的方法，其中，根据预设的异常坠地匹配的参数条件，以及由所述离地遥测数据确定的姿态关联参数的参数值和部件关联参数的参数值，检测所述无人机是否属于异常坠地的步骤，包括：
    根据所述离地遥测数据对应的滚转角参数，判断所述无人机是否满足预设的异常坠地匹配的第一参数条件；
    在确定所述无人机不满足预设的异常坠地匹配的第一参数条件时，根据所述离地遥测数据对应的俯仰角参数，判断所述无人机是否满足预设的异常坠地匹配的第二参数条件；
    在确定所述无人机不满足预设的异常坠地匹配的第二参数条件时，针对每一个架次，根据所述架次中的离地遥测数据对应的滚转角参数、俯仰角参数、以及电机转速参数判断所述无人机是否满足预设的异常坠地匹配的第三参数条件。
  6. 根据权利要求5所述的方法，其中，根据所述离地遥测数据对应的滚转角参数，判断所述无人机是否满足预设的异常坠地匹配的第一参数条件的步骤，包括：
    获取所述离地遥测数据中滚转角参数大于第一滚转角门限的第一离地遥测数据；
    获取所述离地遥测数据中滚转角参数小于第二滚转角门限的第二离地遥测数据；
    在确定所述第一离地遥测数据的数据个数等于0，或者所述第二离地遥测数据的数据个数等于0时，确定所述无人机不满足预设的异常坠地匹配的第一参数条件。
  7. 根据权利要求5所述的方法，其中，根据所述离地遥测数据对应的俯仰角参数，判断所述无人机是否满足预设的异常坠地匹配的第二参数条件的步骤，包括：
    获取所述离地遥测数据中俯仰角参数大于第一俯仰角门限的第三离地遥测数据；
    获取所述离地遥测数据中俯仰角参数小于第二俯仰角门限的第四离地遥测数据；
    在确定所述第三离地遥测数据的数据个数等于0，或者所述第四离地遥测数据的数据个数等于0时，确定所述无人机不满足预设的异常坠地匹配的第二参数条件。
  8. 一种无人机异常坠地的检测装置，包括：
    数据获取模块，被配置为获取待检测的无人机的遥测数据；
    数据提取模块，被配置为在所述遥测数据中，提取有效的离地遥测数据；
    无人机检测模块，被配置为根据预设的异常坠地匹配的参数条件，以及由所述离地遥测数据确定的姿态关联参数的参数值和部件关联参数的参数值，检测所述无人机是否属于异常坠地。
  9. 一种计算机设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1-7中任一所述的无人机异常坠地的检测方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-7中任一所述的无人机异常坠地的检测方法。
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