Methods, devices, electronic equipment, media and products for predicting the remaining flight time of unmanned aerial vehicles (UAVs)

Abstract

This application discloses a method, apparatus, electronic device, medium, and product for predicting the remaining flight time of a drone. The method includes: acquiring, in real-time, the drone's equipment status, operating environment data, drone flight data, current air density, wind speed correction coefficient, battery aging correction coefficient, and battery temperature correction coefficient during drone operation; calculating the total power consumption of the entire drone under the current flight state; correcting the total power consumption to obtain a corrected average power consumption for endurance assessment; calculating the current effective remaining battery power; and calculating the theoretical remaining flight time under the current flight state without considering return-to-home constraints. This method can solve the problems of large prediction errors and low reliability caused by ignoring the dynamic flight environment and real-time battery status.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a method, apparatus, electronic device, readable storage medium, and computer program product for predicting the remaining flight time of a UAV. Background Technology

[0002] With drones now widely used in complex mission scenarios such as agricultural plant protection, geographic surveying, and logistics delivery, accurate prediction of remaining flight time is crucial for ensuring mission safety and improving operational efficiency. Currently, mainstream prediction methods rely on simple models based on battery voltage and current integration to estimate remaining battery power, combined with historical average power consumption to extrapolate time. However, this method fails to fully consider the real-time impact of dynamic changes in the flight environment, such as air density and wind speed, on the propulsion system, nor does it correct for performance degradation caused by battery aging and temperature. This results in significant discrepancies between predicted and actual flight time in high-load, complex weather, or battery-powered missions, failing to provide operators with reliable battery warnings and increasing the risk of the aircraft losing control or crashing due to depleted battery power. Summary of the Invention

[0003] In view of the above problems, this application provides a method, device, electronic device, readable storage medium and computer program product for predicting the remaining flight time of a UAV, which can solve the problems of large prediction error and low reliability caused by ignoring the dynamic flight environment and real-time battery status.

[0004] Firstly, this application provides a method for predicting the remaining flight time of an unmanned aerial vehicle (UAV), including:

[0005] During drone operation, real-time acquisition of drone equipment status, operating environment data, and drone flight data is performed.

[0006] The current air density and wind speed correction coefficients are obtained based on the operating environment data, and the battery aging correction coefficients and battery temperature correction coefficients are obtained based on the drone equipment status.

[0007] Based on the current air density, the drone equipment status, the drone flight data, preset drone parameters, preset fixed power consumption of onboard equipment, and preset efficiency mapping table, calculate the total power consumption of the entire aircraft under the current flight state.

[0008] The total power consumption is corrected based on the battery aging correction coefficient and the wind speed correction coefficient to obtain the corrected average power consumption for range evaluation.

[0009] Calculate the current effective remaining power based on the battery temperature correction coefficient and the current remaining battery power of the drone;

[0010] The theoretical remaining flight time, without considering return-to-home constraints, is calculated based on the corrected average power consumption, preset flight parameters, and effective remaining power.

[0011] In the above technical solution, the method can fully integrate the dynamic flight environment and real-time battery status parameters to accurately correct power consumption and remaining power, thereby effectively reducing prediction deviations in complex scenarios, providing operators with reliable flight time references, and thus improving the safety and operational efficiency of UAV mission execution.

[0012] In some implementations, the parameters of the UAV itself include at least the number of motors, propeller area, equivalent drag area of ​​the fuselage, and total mass of the UAV;

[0013] The preset flight parameters include at least vertical climb efficiency, return cruise speed, hover power consumption, and cruise power consumption.

[0014] The fixed power consumption of the airborne equipment is the average fixed power consumption value of each airborne device.

[0015] The efficiency mapping table includes at least a motor ESC efficiency comparison table and a propeller efficiency interpolation table;

[0016] The drone equipment status includes at least the real-time tension, speed, and torque of each motor, as well as the remaining battery power, estimated battery health, and real-time battery temperature.

[0017] The operating environment data includes at least the current air pressure, ambient temperature, and current wind speed;

[0018] The UAV flight data includes at least its current flight speed, its current angle of attack, the angle between its current heading and the current wind speed, the horizontal distance from its current location to the return point, and the vertical height difference between the return points.

[0019] In the above technical solution, the method can integrate multi-dimensional equipment status, environmental data and flight parameters, and significantly improve the accuracy and reliability of remaining flight time prediction under complex working conditions by accurately calculating the total power consumption and combining multiple correction coefficients for optimization evaluation, thus providing strong support for UAV mission planning and safe return.

[0020] In some implementations, obtaining the current air density and wind speed correction coefficients based on the operating environment data includes:

[0021] Obtain the current wind speed, current air pressure, and ambient temperature based on the aforementioned operating environment data;

[0022] Calculate the current air density based on the current air pressure and the ambient temperature;

[0023] When the current wind speed exceeds a preset wind speed threshold and the angle between the current heading of the UAV and the current wind speed meets a preset condition, a wind speed correction coefficient is obtained based on the current wind speed.

[0024] In the above technical solution, the method can accurately calculate air density by combining environmental data, and dynamically adapt the wind speed correction coefficient for scenarios with wind speeds exceeding the threshold and specific heading angles, further refining the assessment of the impact of environmental factors on power consumption, and improving the accuracy and scenario adaptability of remaining flight time prediction.

[0025] In some implementations, obtaining the battery aging correction factor and the battery temperature correction factor based on the drone equipment status includes:

[0026] Based on the drone equipment status, obtain the estimated battery health value and real-time battery temperature;

[0027] The battery aging correction coefficient is obtained based on the battery health estimate, and the battery temperature correction coefficient is obtained based on the real-time battery temperature.

[0028] In the above technical solution, the method can accurately obtain aging and temperature correction coefficients by combining battery health and real-time temperature, and specifically compensate for the impact of battery performance degradation, thereby reducing the interference of battery state fluctuations on flight time prediction.

[0029] In some implementations, calculating the total power consumption of the entire aircraft under the current flight state based on the current air density, the UAV equipment status, the UAV flight data, preset UAV self-parameters, preset fixed power consumption of onboard equipment, and a preset efficiency mapping table includes:

[0030] The system obtains real-time motor status data for each motor based on the drone's equipment status, as well as the number of motors, propeller area, and equivalent drag area of ​​the fuselage based on the drone's own parameters, and obtains the current flight speed based on the drone's flight data.

[0031] The induced power of each motor on the UAV is calculated based on the real-time motor status data, the propeller area, and the current air density.

[0032] Based on the equivalent drag area of ​​the fuselage, the current flight speed, the current air density, and the number of motors, calculate the drag-distributed power of the fuselage drag to each motor;

[0033] The torque and power of each motor are calculated based on the real-time motor status data.

[0034] Calculate the total shaft power of each motor based on the induced power, the resistance-shared power, and the torque power;

[0035] Based on the real-time motor status data and the preset efficiency mapping table, determine the system efficiency value corresponding to each motor;

[0036] Calculate the actual power consumption of each motor based on the system efficiency value and the total shaft power;

[0037] Calculate the total power consumption of the entire aircraft under the current flight conditions based on all the actual power consumptions mentioned.

[0038] In the above technical solution, the method can accurately calculate the energy consumption of each motor by decomposing the motor induced power, resistance-shared power and torque power in layers, and combining the UAV's own parameters, real-time status and efficiency mapping table, and then sum up to obtain the total power consumption of the whole machine, thus realizing the refinement and transparency of power consumption calculation.

[0039] In some embodiments, the step of correcting the total power consumption based on the battery aging correction factor and the wind speed correction factor to obtain the corrected average power consumption for range assessment includes:

[0040] The total power consumption is initially corrected based on the battery aging correction coefficient to obtain the first corrected power consumption.

[0041] When it is determined from the operating environment data that the current wind speed meets the wind speed correction condition, the first corrected power consumption is corrected according to the wind speed correction coefficient to obtain the final power consumption value.

[0042] Calculate the total real-time power consumption of the entire machine based on the fixed power consumption of the airborne equipment and the final power consumption.

[0043] Obtain the total real-time power consumption of the entire machine within a preset time period;

[0044] The total real-time power consumption of the entire machine is weighted and averaged to obtain the average power consumption value.

[0045] When the drone's current flight speed exceeds a preset speed threshold, obtain the angle relationship between the current wind speed and the current heading;

[0046] The average power consumption value is corrected based on the angle relationship to obtain a second corrected power consumption;

[0047] The second corrected power consumption is corrected according to the battery aging correction coefficient to obtain the final corrected average power consumption used for battery life evaluation.

[0048] In the above technical solution, the method can achieve accurate calibration of total power consumption by using multiple rounds of progressive correction (initial correction for battery aging, correction for wind speed conditions) and weighted averaging processing over a preset time period, combined with dynamic optimization of flight speed threshold and heading-wind speed angle, thus providing average power consumption data that is more in line with actual operating conditions for calculating remaining flight time.

[0049] In some implementations, after calculating the theoretical remaining flight time in the current flight state without considering return-to-home constraints based on the corrected average power consumption, preset flight parameters, and the effective remaining battery power, the process includes:

[0050] Calculate the total energy required for a safe return based on the drone's own parameters, preset flight parameters, and drone flight data;

[0051] Based on the effective remaining power and the total energy required for a safe return, calculate the target remaining power after a safe return.

[0052] Based on the target remaining power and the corrected average power consumption, calculate the safe time margin that can be used to continue the current task or return home at an ease.

[0053] In the above technical solution, this method can further accurately calculate the energy required for safe return and the remaining battery power of the target based on the theoretical remaining flight time, clarify the safe time margin for mission continuation and calm return, provide operators with a more comprehensive basis for decision-making, effectively avoid the risk of return due to insufficient battery power, and ensure the dual safety of UAV mission execution and return.

[0054] In some implementations, calculating the total energy required for a safe return based on the UAV's own parameters, preset flight parameters, and the UAV's flight data includes:

[0055] The total mass of the UAV is obtained based on its own parameters, and the return cruise speed, cruise power consumption, hovering power consumption, return horizontal distance from the current position to the return point and return vertical height difference are obtained based on the UAV flight data, and the vertical climb efficiency is obtained based on the preset flight parameters.

[0056] Calculate the energy required for the return climb phase based on the total mass of the UAV, the vertical altitude difference during return, and the vertical climb efficiency.

[0057] Calculate the energy required for the cruise phase based on the return horizontal distance, the return cruise speed, and the cruise power consumption.

[0058] Calculate the total energy required for a safe return based on the energy required for the return climb phase, the energy required for the cruise phase, and the hovering power consumption.

[0059] In the above technical solution, the method can break down the entire return process (climb, cruise, hover) and accurately calculate the energy required for each stage by combining core parameters such as the total mass of the UAV, the return distance and altitude difference, so as to realize the refined calculation of the total energy required for safe return and provide an accurate basis for subsequent safety time margin assessment.

[0060] In some embodiments, the method further includes:

[0061] Output the theoretical remaining flight time and the safety time margin;

[0062] When the safety time margin is not greater than a preset time threshold, the drone is determined to be low on power, and an alarm message is output indicating that the drone is low on power and needs to initiate an emergency return procedure or execute a forced landing plan.

[0063] In the above technical solution, the method can clearly output core flight time data and safety margin, and promptly trigger alarms and provide emergency handling guidance when the safety time margin reaches the threshold, helping operators to quickly grasp the power status, make accurate decisions, and effectively avoid the risk of drone loss of control or crash.

[0064] Secondly, this application provides a device for predicting the remaining flight time of an unmanned aerial vehicle (UAV), comprising:

[0065] The first acquisition unit is used to acquire drone equipment status, operating environment data and drone flight data in real time during drone operation;

[0066] The second acquisition unit is used to acquire the current air density and wind speed correction coefficient based on the operating environment data.

[0067] The third acquisition unit is used to acquire the battery aging correction coefficient and the battery temperature correction coefficient according to the state of the UAV equipment.

[0068] The first calculation unit is used to calculate the total power consumption of the entire aircraft under the current flight state based on the current air density, the status of the UAV equipment, the flight data of the UAV, the preset UAV self parameters, the preset fixed power consumption of the airborne equipment and the preset efficiency mapping table.

[0069] The correction unit is used to correct the total power consumption according to the battery aging correction coefficient and the wind speed correction coefficient to obtain the corrected average power consumption for range evaluation.

[0070] The second calculation unit is used to calculate the current effective remaining power based on the battery temperature correction coefficient and the current remaining battery power of the drone.

[0071] The third calculation unit is used to calculate the theoretical remaining flight time under the current flight state without considering the return-to-home constraint, based on the corrected average power consumption, preset flight parameters and the effective remaining power.

[0072] In the above technical solution, the device can fully integrate the dynamic flight environment and real-time battery status parameters to accurately correct power consumption and remaining power, thereby effectively reducing prediction deviations in complex scenarios, providing operators with reliable flight time references, and thus improving the safety and operational efficiency of UAV mission execution.

[0073] Thirdly, this application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the UAV remaining flight time prediction method as described in any one of the first aspects.

[0074] Fourthly, this application provides a readable storage medium storing a computer program, which, when executed by a processor, performs the unmanned aerial vehicle (UAV) remaining flight time prediction method described in any one of the first aspects.

[0075] Fifthly, this application provides a computer program product, which includes a computer program that, when executed by a processor, performs the unmanned aerial vehicle (UAV) remaining flight time prediction method described in any one of the first aspects.

[0076] The beneficial effects of this application are: it can fully integrate dynamic flight environment and real-time battery status parameters to accurately correct power consumption and remaining power, thereby effectively reducing prediction deviation in complex scenarios, providing operators with reliable flight time references, and thus improving the safety and operational efficiency of UAV mission execution. Attached Figure Description

[0077] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a flowchart illustrating the method for predicting the remaining flight time of a drone in some embodiments of this application;

[0079] Figure 2 This is a schematic diagram of the structure of the UAV remaining flight time prediction device in some embodiments of this application;

[0080] Figure 3This is a schematic diagram of the structure of an electronic device in some embodiments of this application. Detailed Implementation

[0081] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0082] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0083] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more (including two), similarly, "multiple sets" refers to two or more sets (including two sets), and "multiple pieces" refers to two or more pieces (including two pieces) unless otherwise explicitly defined.

[0084] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0085] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0086] Current methods fail to adequately consider the real-time impact of dynamic changes in the flight environment, such as air density and wind speed, on the power system. They also fail to correct for performance degradation caused by battery aging and temperature. As a result, in actual missions with high loads, complex weather conditions, or declining battery performance, the predicted results deviate significantly from the actual flight time. This makes it impossible to provide operators with reliable power warnings and increases the risk of the aircraft losing control or crashing due to power depletion.

[0087] To address the aforementioned technical issues, this application provides a method for predicting the remaining flight time of a UAV. This method fully integrates dynamic flight environment and real-time battery status parameters to accurately correct power consumption and remaining battery power, thereby effectively reducing prediction deviations in complex scenarios, providing operators with reliable flight time references, and ultimately improving the safety and operational efficiency of UAV mission execution.

[0088] like Figure 1 As shown, some embodiments of this application provide a method for predicting the remaining flight time of a UAV, which includes:

[0089] S101. During drone operation, real-time acquisition of drone equipment status, operating environment data, and drone flight data;

[0090] S102. Obtain the current air density and wind speed correction coefficients based on the operating environment data, and obtain the battery aging correction coefficients and battery temperature correction coefficients based on the drone equipment status.

[0091] S103. Based on the current air density, UAV equipment status, UAV flight data, preset UAV parameters, preset fixed power consumption of onboard equipment, and preset efficiency mapping table, calculate the total power consumption of the entire aircraft under the current flight state.

[0092] S104. Correct the total power consumption according to the battery aging correction coefficient and the wind speed correction coefficient to obtain the corrected average power consumption for range evaluation.

[0093] S105. Calculate the current effective remaining power based on the battery temperature correction factor and the current remaining battery power of the drone;

[0094] S106. Calculate the theoretical remaining flight time under the current flight state without considering return-to-home constraints, based on the corrected average power consumption, preset flight parameters, and effective remaining power.

[0095] In some embodiments, the drone equipment status refers to the real-time tension, speed, and torque of each motor, as well as the remaining battery power, estimated battery health, and real-time battery temperature.

[0096] In some embodiments, operating environment data refers to current air pressure, ambient temperature, and current wind speed.

[0097] In some embodiments, UAV flight data refers to its current flight speed, its own angle of attack, the angle between its current heading and the current wind speed, the horizontal distance from its current location to the return point, and the vertical height difference between the return points.

[0098] In some embodiments, current air density refers to a parameter that reflects the density of the current ambient air, calculated based on the current air pressure and ambient temperature from the operating environment data.

[0099] In some embodiments, the wind speed correction coefficient refers to a coefficient determined based on the current wind speed for correcting total power consumption when the current wind speed exceeds a preset wind speed threshold and the angle between the current heading of the UAV and the current wind speed meets a preset condition.

[0100] In some embodiments, the battery aging correction factor refers to a factor obtained from the battery health estimate in the drone equipment state, used to compensate for performance degradation caused by battery aging.

[0101] In some embodiments, the battery temperature correction factor refers to a factor obtained based on the real-time battery temperature in the drone device status, used to correct the impact of temperature on battery performance.

[0102] In some embodiments, the preset drone parameters refer to the pre-defined number of motors, propeller area, equivalent drag area of ​​the fuselage, and total mass of the drone.

[0103] In some embodiments, the preset fixed power consumption of airborne equipment refers to the preset average fixed power consumption value of each airborne device.

[0104] In some embodiments, the preset efficiency mapping table refers to a pre-defined mapping table that includes at least a motor ESC efficiency comparison table and a propeller efficiency interpolation table.

[0105] In some embodiments, total power consumption refers to the total power consumption of the entire drone in the current flight state, calculated based on the current air density, drone equipment status, drone flight data, preset drone parameters, preset fixed power consumption of onboard equipment, and preset efficiency mapping table.

[0106] In some embodiments, the corrected average power consumption refers to the average power consumption used for range assessment after correcting the total power consumption according to the battery aging correction factor and the wind speed correction factor.

[0107] In some embodiments, effective remaining power refers to the remaining power that reflects the actual usable battery capacity, calculated based on the battery temperature correction factor and the current remaining battery power of the drone.

[0108] In some embodiments, preset flight parameters refer to parameters that are pre-set and include at least vertical climb efficiency, return cruise speed, hover power consumption, and cruise power consumption.

[0109] In some embodiments, the theoretical remaining flight time refers to the duration that the drone can continue flying in the current flight state without considering return-to-home constraints, calculated based on the corrected average power consumption, preset flight parameters, and effective remaining power.

[0110] For example, this method can multiply the current remaining battery power by the battery temperature correction factor to obtain the actual available remaining battery power (i.e., the effective remaining battery power). Then, the actual available remaining battery power is divided by the corrected average power consumption to obtain the theoretical remaining flight time.

[0111] In the above embodiments, the method can fully integrate dynamic flight environment and real-time battery status parameters to accurately correct power consumption and remaining power, thereby effectively reducing prediction deviation in complex scenarios, providing operators with reliable flight time references, and thus improving the safety and operational efficiency of UAV mission execution.

[0112] In some embodiments, the parameters of the drone itself include at least the number of motors, propeller area, equivalent drag area of ​​the fuselage, and total mass of the drone.

[0113] The preset flight parameters include at least vertical climb efficiency, return cruise speed, hover power consumption, and cruise power consumption;

[0114] The fixed power consumption of airborne equipment is the average fixed power consumption value of all airborne equipment.

[0115] The efficiency mapping table should include at least a motor ESC efficiency comparison table and a propeller efficiency interpolation table;

[0116] The drone equipment status includes at least the real-time tension, speed, and torque of each motor, as well as the remaining battery power, estimated battery health, and real-time battery temperature.

[0117] The operating environment data should include at least the current air pressure, ambient temperature, and current wind speed;

[0118] The drone flight data should include at least its current flight speed, its angle of attack, the angle between its current heading and the current wind speed, the horizontal distance from its current location to the return point, and the vertical height difference between the return point and the return point.

[0119] In some embodiments, vertical climb efficiency refers to the ratio of the effective energy actually used by the UAV to overcome its own gravity and achieve altitude gain during vertical ascent to the total energy output by the motor. This vertical climb efficiency can be 0.68, meaning that approximately 68% of the motor output energy can be converted into effective work for vertical climb, while the remaining energy is mainly consumed by air resistance, motor / ESC losses, and propeller aerodynamic losses.

[0120] In some embodiments, the method can test the efficiency at different speeds and tensions using a test bench and compile the results into a motor-electromechanical controller efficiency comparison table.

[0121] In some embodiments, the method can test efficiency at different airspeeds and rotational speeds from 0 to 25 m / s and compile the results into a propeller efficiency table (supporting interpolation calculation).

[0122] In some embodiments, the torque in the real-time acquired drone device status is allocated by the controller, and the battery health is estimated online.

[0123] In some embodiments, the self-flight angle of attack is the angle between the fuselage and the airflow.

[0124] For example, the fixed power consumption of airborne equipment can be the stable power consumption of devices such as flight controllers, sensors, image transmission, and cameras (average of multiple measurements).

[0125] In the above embodiments, the method can integrate multi-dimensional equipment status, environmental data and flight parameters, and significantly improve the accuracy and reliability of remaining flight time prediction under complex working conditions by accurately calculating the total power consumption and combining multiple correction coefficients for optimization evaluation, thus providing strong support for UAV mission planning and safe return.

[0126] In some embodiments, obtaining the current air density and wind speed correction coefficients based on operating environment data includes:

[0127] Obtain the current wind speed, current air pressure, and ambient temperature based on the operating environment data;

[0128] Calculate the current air density based on the current air pressure and ambient temperature;

[0129] When the current wind speed exceeds the preset wind speed threshold and the angle between the drone's current heading and the current wind speed meets the preset conditions, the wind speed correction coefficient is obtained based on the current wind speed.

[0130] For example, this method can calculate the air density using the gas state equation based on the current air pressure and ambient temperature (the higher the air pressure and the lower the temperature, the greater the density).

[0131] For example, this method can calculate a wind speed correction coefficient when the current wind speed exceeds a preset wind speed threshold (e.g., 8 m / s) and the angle between the wind speed and the drone's current heading is less than 90°. The coefficient is less than 1 when the drone is running with the wind, thus offsetting some of the power consumption.

[0132] In the above embodiments, the method can accurately calculate air density by combining environmental data, and dynamically adapt the wind speed correction coefficient for scenarios with wind speeds exceeding the threshold and specific heading angles, further refining the assessment of the impact of environmental factors on power consumption, and improving the accuracy and scenario adaptability of remaining flight time prediction.

[0133] In some embodiments, obtaining the battery aging correction factor and the battery temperature correction factor based on the drone equipment status includes:

[0134] Obtain battery health estimates and real-time battery temperature based on the drone's equipment status;

[0135] The battery aging correction factor is obtained based on the battery health estimate, and the battery temperature correction factor is obtained based on the real-time battery temperature.

[0136] For example, the lower the estimated battery health value, the larger the battery aging correction factor (for example, when the estimated battery health value is 80%, the battery aging correction factor is 1.1, which means that the power consumption needs to be calculated by 10%).

[0137] For example, the greater the deviation of the real-time battery temperature from 25°C, the smaller the battery temperature correction factor (for example, when the real-time battery temperature is 15°C or 35°C, the battery temperature correction factor is about 0.94, and the remaining power needs to be reduced by 94%).

[0138] In the above embodiments, the method can accurately obtain aging and temperature correction coefficients by combining battery health and real-time temperature, and specifically compensate for the impact of battery performance degradation, thereby reducing the interference of battery state fluctuations on flight time prediction.

[0139] In some embodiments, the total power consumption of the entire aircraft under the current flight state is calculated based on the current air density, the drone equipment status, the drone flight data, preset drone parameters, preset fixed power consumption of onboard equipment, and a preset efficiency mapping table, including:

[0140] The system obtains real-time motor status data for each motor based on the drone's equipment status, as well as the number of motors, propeller area, and equivalent drag area of ​​the fuselage based on the drone's own parameters, and obtains the drone's current flight speed based on the drone's flight data.

[0141] The induced power of each motor on the drone is calculated based on real-time motor status data, propeller area, and current air density.

[0142] Based on the equivalent drag area of ​​the fuselage, the current flight speed, the current air density, and the number of motors, calculate the drag-distributed power of the fuselage drag to each motor;

[0143] The torque and power of each motor are calculated based on real-time motor status data.

[0144] Calculate the total shaft power of each motor based on induced power, resistance-shared power, and torque power;

[0145] Based on real-time motor status data and a preset efficiency mapping table, determine the system efficiency value corresponding to each motor;

[0146] Calculate the actual power consumption of each motor based on the system efficiency value and total shaft power;

[0147] Calculate the total power consumption of the entire aircraft under the current flight conditions based on all actual power consumption.

[0148] In some embodiments, induced power refers to the additional power consumption that comes with the propeller generating the thrust required for flight. It can be calculated by determining the induced velocity using the thrust, air density, and propeller area, and then multiplying the thrust by the induced velocity.

[0149] In some embodiments, fuselage drag power refers to the power consumption generated by air resistance during the flight of the UAV. This power consumption is shared equally by all motors, and the power of fuselage drag power allocated to each motor is called drag-shared power.

[0150] In some embodiments, torque power refers to the power consumption consumed by the motor to overcome its own torque during rotation, which can be calculated by multiplying torque by speed.

[0151] In some embodiments, the total shaft power of a single motor is the sum of induced power, resistance-shared power and torque power, that is, the sum of the three is the total shaft power of a single motor.

[0152] In some embodiments, the efficiency of the corrected motor and propeller can be obtained by querying a preset efficiency mapping table based on the real-time speed and thrust of the current motor, and obtaining the combined efficiency of the motor and ESC and the propeller efficiency respectively; then, the total shaft power of a single motor is divided by the product of the combined efficiency of the motor and ESC and the propeller efficiency to obtain the actual power consumption of a single motor.

[0153] In some embodiments, the method can sum up the actual power consumption of each motor of the drone to obtain the total power consumption of the entire drone.

[0154] In the above embodiments, the method can accurately calculate the energy consumption of each motor by decomposing the motor induced power, resistance-shared power and torque power in layers, and combining the UAV's own parameters, real-time status and efficiency mapping table, and then sum up to obtain the total power consumption of the whole machine, thus realizing the refinement and transparency of power consumption calculation.

[0155] In some embodiments, the total power consumption is corrected according to a battery aging correction factor and a wind speed correction factor to obtain a corrected average power consumption for range evaluation, including:

[0156] The total power consumption is initially corrected based on the battery aging correction factor to obtain the first corrected power consumption.

[0157] When the current wind speed meets the wind speed correction condition based on the operating environment data, the first correction power consumption is corrected according to the wind speed correction coefficient to obtain the final power consumption value.

[0158] Calculate the total real-time power consumption of the entire machine based on the fixed power consumption and final power consumption of the airborne equipment.

[0159] Obtain the total real-time power consumption of the entire machine within a preset time period;

[0160] The total real-time power consumption of the entire machine is weighted and averaged to obtain the average power consumption value.

[0161] When the drone's current flight speed exceeds a preset speed threshold, obtain the angle relationship between the current wind speed and the current heading;

[0162] The average power consumption value is corrected based on the angle relationship to obtain the second corrected power consumption;

[0163] The second corrected power consumption is corrected based on the battery aging correction factor to obtain the final corrected average power consumption used for range evaluation.

[0164] In some embodiments, the method can first multiply the total power consumption by the battery aging correction coefficient to obtain the first corrected power consumption; if the current wind speed meets the preset wind speed correction condition, then multiply the first corrected power consumption by the wind speed correction coefficient to finally obtain the corrected final power consumption value, thereby completing the superposition of correction coefficients.

[0165] In some embodiments, the method can calculate the overall real-time total power consumption of the UAV by adding the fixed power consumption of the airborne equipment (i.e., the basic power consumption of the flight controller, sensors, etc.) to the final power consumption (i.e., the power consumption generated by the motor driving the propeller).

[0166] In some embodiments, the method can perform weighted averaging on the total real-time power consumption of the entire machine collected over a preset time period (e.g., within the last 30 seconds) to obtain an average power consumption value. More recently collected data is given higher weight, which can reduce the impact of instantaneous power consumption fluctuations on the evaluation results and achieve the calculation of the average power consumption value.

[0167] In some embodiments, when the current flight speed of the drone exceeds a preset speed threshold (e.g., 8 m / s), the method first dynamically corrects the average power consumption value based on the angle between the current wind speed and the current heading (power consumption decreases accordingly in tailwind scenarios and increases accordingly in headwind scenarios) to obtain a second corrected power consumption; then, the second corrected power consumption is further corrected using a battery aging correction coefficient to ensure that the power consumption data is consistent with the actual performance state of the battery, thereby completing the correction of the average power consumption and obtaining the final corrected average power consumption used for range evaluation.

[0168] In the above embodiments, the method can achieve accurate calibration of total power consumption by using multiple rounds of progressive correction (initial correction for battery aging, correction for wind speed conditions) and weighted averaging processing over a preset time period, combined with dynamic optimization of flight speed threshold and heading-wind speed angle, thus providing average power consumption data that is more in line with actual operating conditions for calculating remaining flight time.

[0169] In some embodiments, after calculating the theoretical remaining flight time in the current flight state without considering return-to-home constraints based on the corrected average power consumption, preset flight parameters, and effective remaining battery power, the process includes:

[0170] Calculate the total energy required for a safe return based on the drone's own parameters, preset flight parameters, and drone flight data;

[0171] Calculate the target remaining power after a safe return based on the available remaining power and the total energy required for a safe return;

[0172] Based on the target remaining power and the corrected average power consumption, calculate the safe time margin that can be used to continue the current task or return safely.

[0173] In some embodiments, the method can first calculate the safe power required for the return flight, and determine the amount of energy that the drone must reserve to complete the safe return flight, that is, the total energy required for the safe return flight.

[0174] In some embodiments, the method can calculate the safe return time, i.e. the safe time margin, by subtracting the total energy required for a safe return from the actual available effective remaining power and then dividing the difference by the corrected average power consumption obtained through multiple rounds of progressive correction.

[0175] In the above embodiments, this method can further accurately calculate the energy required for a safe return and the remaining target power based on the theoretical remaining flight time, clarify the safe time margin for mission continuation and a smooth return, provide operators with a more comprehensive basis for decision-making, effectively avoid the risk of return due to insufficient power, and ensure the dual safety of UAV mission execution and return.

[0176] In some embodiments, the total energy required for a safe return is calculated based on the drone's own parameters, preset flight parameters, and drone flight data, including:

[0177] The total mass of the drone is obtained based on its own parameters, and the return cruise speed, cruise power consumption, hovering power consumption, return horizontal distance and return vertical height difference from the current position to the return point are obtained based on the drone flight data, and the vertical climb efficiency is obtained based on the preset flight parameters.

[0178] Calculate the energy required for the return climb phase based on the total mass of the UAV, the vertical height difference during return, and the vertical climb efficiency.

[0179] Calculate the energy required for the cruise phase based on the horizontal distance of return, the cruise speed of return, and the cruise power consumption.

[0180] Calculate the total energy required for a safe return based on the energy required during the return climb phase, the energy required during the cruise phase, and the power consumption during hovering.

[0181] In some embodiments, the method can obtain the climb energy required for the return climb phase by multiplying the total mass of the UAV, the gravitational acceleration and the climb height difference (i.e., the return vertical height difference) by the vertical climb efficiency.

[0182] In some embodiments, the method may first calculate the ratio of the return horizontal distance to the return cruise speed to obtain the cruise flight time, and then calculate the product of the cruise power consumption and the cruise flight time to obtain the cruise energy required for the return cruise phase, i.e., the energy required for the cruise phase.

[0183] In some embodiments, the method may reserve power for landing buffer, specifically, a power limit sufficient for a 5-minute hovering requirement.

[0184] In some embodiments, the method can add the energy required for the climb phase, the energy required for the cruise phase, the reserved power, and the energy required for the hover phase calculated based on the hover power consumption to obtain the total safe power required for the UAV to complete a safe return, i.e., the total energy required for a safe return.

[0185] In the above embodiments, the method can break down the entire return process (climb, cruise, hover) and accurately calculate the energy required for each stage by combining core parameters such as the total mass of the UAV, the return distance and altitude difference, so as to realize the refined calculation of the total energy required for safe return and provide an accurate basis for subsequent safety time margin assessment.

[0186] In some embodiments, the method further includes:

[0187] Output the theoretical remaining flight time and safety time margin;

[0188] When the safety time margin is no greater than the preset time threshold, the system determines that the drone's current battery is low and outputs an alarm message indicating that the low battery requires initiating an emergency return procedure or executing a forced landing plan.

[0189] For example, if the safety time margin is greater than or equal to a preset time threshold (e.g., when the preset time threshold is 0), it is considered that the remaining power is sufficient for a safe return, and the mission can continue or the mission can be completed as planned.

[0190] If the safety time margin is less than the preset time threshold (for example, when the preset time threshold is 0), it is considered that the remaining power is insufficient, and an emergency return or forced landing prompt needs to be triggered immediately.

[0191] In the above embodiments, the method can clearly output core flight time data and safety margin, and promptly trigger alarms and provide emergency handling guidance when the safety time margin reaches the threshold, helping operators to quickly grasp the battery status, make accurate decisions, and effectively avoid the risk of drone loss of control or crash.

[0192] Figure 2 A schematic diagram of a UAV remaining flight time prediction device is shown. It should be understood that this device is related to... Figure 1 The method executed in the middle corresponds to the steps involved in the aforementioned method. The specific functions and effects of the device can be found in the description above. To avoid repetition, detailed descriptions are omitted here.

[0193] The drone's remaining flight time prediction device includes:

[0194] The first acquisition unit 210 is used to acquire drone equipment status, operating environment data and drone flight data in real time during drone operation;

[0195] The second acquisition unit 220 is used to acquire the current air density and wind speed correction coefficient based on the operating environment data;

[0196] The third acquisition unit 230 is used to acquire the battery aging correction coefficient and the battery temperature correction coefficient according to the status of the UAV equipment.

[0197] The first calculation unit 240 is used to calculate the total power consumption of the whole machine under the current flight state based on the current air density, the status of the UAV equipment, the UAV flight data, the preset UAV self parameters, the preset fixed power consumption of the airborne equipment and the preset efficiency mapping table.

[0198] The correction unit 250 is used to correct the total power consumption according to the battery aging correction coefficient and the wind speed correction coefficient to obtain the corrected average power consumption for range evaluation.

[0199] The second calculation unit 260 is used to calculate the current effective remaining power based on the battery temperature correction coefficient and the current remaining battery power of the drone.

[0200] The third calculation unit 270 is used to calculate the theoretical remaining flight time under the current flight state without considering the return-to-home constraint, based on the corrected average power consumption, preset flight parameters and effective remaining power.

[0201] In some embodiments, the parameters of the drone itself include at least the number of motors, propeller area, equivalent drag area of ​​the fuselage, and total mass of the drone.

[0202] The preset flight parameters include at least vertical climb efficiency, return cruise speed, hover power consumption, and cruise power consumption;

[0203] The fixed power consumption of airborne equipment is the average fixed power consumption value of all airborne equipment.

[0204] The efficiency mapping table should include at least a motor ESC efficiency comparison table and a propeller efficiency interpolation table;

[0205] The drone equipment status includes at least the real-time tension, speed, and torque of each motor, as well as the remaining battery power, estimated battery health, and real-time battery temperature.

[0206] The operating environment data should include at least the current air pressure, ambient temperature, and current wind speed;

[0207] The drone flight data should include at least its current flight speed, its angle of attack, the angle between its current heading and the current wind speed, the horizontal distance from its current location to the return point, and the vertical height difference between the return point and the return point.

[0208] In some embodiments, the second acquisition unit 220 includes:

[0209] The first acquisition subunit 221 is used to acquire the current wind speed, current air pressure and ambient temperature based on the operating environment data;

[0210] The first calculation subunit 222 is used to calculate the current air density based on the current air pressure and ambient temperature;

[0211] The first acquisition subunit 221 is also used to acquire a wind speed correction coefficient based on the current wind speed when the current wind speed exceeds a preset wind speed threshold and the angle between the current heading of the UAV and the current wind speed meets a preset condition.

[0212] In some embodiments, the third acquisition unit 230 is specifically used to acquire an estimated battery health value and a real-time battery temperature based on the drone device status.

[0213] The third acquisition unit 230 is also used to acquire the battery aging correction coefficient based on the battery health estimate and the battery temperature correction coefficient based on the real-time battery temperature.

[0214] In some embodiments, the first computing unit 240 includes:

[0215] The second acquisition subunit 241 is used to acquire real-time motor status data of each motor according to the UAV equipment status, acquire the number of motors, propeller area and equivalent drag area of ​​the fuselage according to the UAV's own parameters, and acquire the current flight speed of the UAV according to the UAV flight data.

[0216] The second calculation subunit 242 is used to calculate the induced power of each motor on the UAV based on real-time motor status data, propeller area and current air density;

[0217] The second calculation subunit 242 is also used to calculate the resistance-assigned power of the fuselage resistance to each motor based on the fuselage equivalent drag area, the current flight speed, the current air density and the number of motors.

[0218] The second calculation subunit 242 is also used to calculate the torque power of each motor based on real-time motor status data;

[0219] The second calculation subunit 242 is also used to calculate the total shaft power of each motor based on the induced power, the resistance-shared power and the torque power.

[0220] Subunit 243 is used to determine the system efficiency value corresponding to each motor based on real-time motor status data and a preset efficiency mapping table.

[0221] The second calculation subunit 242 is also used to calculate the actual power consumption of each motor based on the system efficiency value and the total shaft power;

[0222] The second calculation subunit 242 is also used to calculate the total power consumption of the whole aircraft under the current flight state based on all actual power consumption.

[0223] In some embodiments, the correction unit 250 includes:

[0224] The correction subunit 251 is used to perform a preliminary correction on the total power consumption based on the battery aging correction coefficient to obtain the first corrected power consumption.

[0225] The correction subunit 251 is also used to correct the first correction power consumption according to the wind speed correction coefficient when the current wind speed is determined to meet the wind speed correction condition based on the operating environment data, so as to obtain the final power consumption value.

[0226] The third calculation subunit 252 is used to calculate the total real-time power consumption of the whole machine based on the fixed power consumption and final power consumption of the airborne equipment.

[0227] The third acquisition subunit 253 is used to acquire the total real-time power consumption of the whole machine collected within a preset time period;

[0228] The weighted subunit 254 is used to perform weighted averaging on the real-time total power consumption of the entire machine to obtain the average power consumption value.

[0229] The third acquisition subunit 253 is also used to acquire the angle relationship between the current wind speed and the current heading when the current flight speed of the UAV exceeds the preset speed threshold.

[0230] The correction subunit 251 is also used to correct the average power consumption value according to the included angle relationship to obtain the second corrected power consumption;

[0231] The correction subunit 251 is also used to correct the second correction power consumption according to the battery aging correction coefficient to obtain the final corrected average power consumption used for range evaluation.

[0232] In some embodiments, the drone remaining flight time prediction device further includes:

[0233] The fourth calculation unit 280 is used to calculate the total energy required for a safe return to home based on the UAV's own parameters, preset flight parameters, and UAV flight data, after the third calculation unit 270 calculates the theoretical remaining flight time in the current flight state without considering the return-to-home constraint based on the corrected average power consumption, preset flight parameters, and effective remaining power.

[0234] The fourth calculation unit 280 is also used to calculate the target remaining power after a safe return, based on the effective remaining power and the total energy required for a safe return.

[0235] The fourth calculation unit 280 is also used to calculate the safe time margin that can be used to continue performing the current task or return safely, based on the target remaining power and the corrected average power consumption.

[0236] In some embodiments, the fourth computing unit 280 includes:

[0237] The fourth acquisition subunit 281 is used to acquire the total mass of the UAV based on its own parameters, and to acquire the return cruise speed, cruise power consumption, hovering power consumption, return horizontal distance and return vertical height difference from the current position to the return point based on the UAV flight data, and to acquire the vertical climb efficiency based on preset flight parameters.

[0238] The fourth calculation subunit 282 is used to calculate the energy required for the return climb phase based on the total mass of the UAV, the vertical height difference during return, and the vertical climb efficiency.

[0239] The fourth calculation subunit 282 is also used to calculate the energy required for the cruise phase based on the return horizontal distance, return cruise speed and cruise power consumption;

[0240] The fourth calculation subunit 282 is also used to calculate the total energy required for a safe return based on the energy required during the return climb phase, the energy required during the cruise phase, and the hovering power consumption.

[0241] In some embodiments, the drone remaining flight time prediction device further includes:

[0242] Output unit 290 is used to output the theoretical remaining flight time and safety time margin;

[0243] The output unit 290 is also used to determine that the current battery level of the drone is low when the safety time margin is not greater than a preset time threshold, and to output an alarm message indicating that the drone needs to start an emergency return procedure or execute a forced landing plan due to insufficient battery level.

[0244] like Figure 3 As shown, this application provides an electronic device 300, which includes a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanism (not shown). The memory 302 stores a computer program that can be executed by the processor 301. When the computing device is running, the processor 301 executes the computer program to perform the method in any of the aforementioned optional implementations.

[0245] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method in any of the aforementioned optional implementations.

[0246] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0247] This application provides a computer program product, which includes a computer program that, when run by a processor, executes the method in any of the aforementioned optional implementations.

[0248] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for predicting the remaining endurance time of a UAV, characterized in that, The application relates to a method for evaluating the remaining flight time of a UAV (unmanned aerial vehicle) in real time. The UAV device state, the running environment data and the UAV flight data are acquired in real time when the UAV is running; The current air density and the wind speed correction coefficient are acquired according to the running environment data, and the battery aging correction coefficient and the battery temperature correction coefficient are acquired according to the UAV device state; The total power consumption of the whole machine in the current flight state is calculated according to the current air density, the UAV device state, the UAV flight data, the preset UAV self parameters, the preset fixed power consumption of the on-board equipment and the preset efficiency mapping table; The total power consumption is corrected according to the battery aging correction coefficient and the wind speed correction coefficient, so that the corrected average power consumption for the endurance evaluation is obtained; The current effective remaining power is calculated according to the battery temperature correction coefficient and the current battery remaining power of the UAV; The theoretical remaining flight time in the current flight state without considering the return constraint is calculated according to the corrected average power consumption, the preset flight parameters and the effective remaining power. 2.The UAV remaining time of flight prediction method of claim 1, wherein, The UAV self parameters at least include the motor number, the propeller area, the body equivalent resistance area and the total mass of the UAV; The preset flight parameters at least include the vertical climbing efficiency, the return cruise speed, the hovering power consumption and the cruising power consumption; The fixed power consumption of the on-board equipment is the average fixed power consumption value of each on-board equipment; The efficiency mapping table at least includes the motor electronic governor efficiency table and the propeller efficiency interpolation table; The UAV device state at least includes the real-time tension, the rotating speed and the torque of each motor, and the remaining power, the battery health estimation value and the real-time battery temperature of the battery; The running environment data at least includes the current air pressure, the environmental temperature and the current wind speed; The UAV flight data at least includes the current self flight speed, the self flight angle of attack, the included angle between the current heading and the current wind speed, the return horizontal distance from the current position to the return point and the return vertical height difference. 3.The UAV remaining time of flight prediction method of claim 1, wherein, The current air density and the wind speed correction coefficient are acquired according to the running environment data, and the battery aging correction coefficient and the battery temperature correction coefficient are acquired according to the UAV device state, which includes: The current wind speed, the current air pressure and the environmental temperature are acquired according to the running environment data; The current air density is calculated according to the current air pressure and the environmental temperature; When the current wind speed exceeds the preset wind speed threshold value and the included angle between the current heading of the UAV and the current wind speed satisfies the preset condition, the wind speed correction coefficient is acquired according to the current wind speed. 4.The UAV remaining time of flight prediction method of claim 1, wherein, The battery health estimation value and the real-time battery temperature are acquired according to the UAV device state; The battery aging correction coefficient is acquired according to the battery health estimation value, and the battery temperature correction coefficient is acquired according to the real-time battery temperature. The total power consumption of the whole machine in the current flight state is calculated according to the current air density, the UAV device state, the UAV flight data, the preset UAV self parameters, the preset fixed power consumption of the on-board equipment and the preset efficiency mapping table, which includes: 5.The UAV remaining time of flight prediction method of claim 1, wherein, ​ According to the unmanned aerial vehicle device state, real-time motor state data of each motor is acquired, and according to the unmanned aerial vehicle itself parameter, motor quantity, propeller area and fuselage equivalent resistance area are acquired, and according to the unmanned aerial vehicle flight data, current self flight speed is acquired; According to the real-time motor state data, the propeller area and the current air density, induced power of each motor on the unmanned aerial vehicle is calculated; According to the fuselage equivalent resistance area, the current self flight speed, the current air density and the motor quantity, resistance allocation power of fuselage resistance allocated to each motor is calculated; According to the real-time motor state data, torque power of each motor is calculated; According to the induced power, the resistance allocation power and the torque power, total shaft power of each motor is calculated; According to the real-time motor state data and a preset efficiency mapping table, corresponding system efficiency values of each motor are determined; According to the system efficiency values and the total shaft power, actual power consumption of each motor is calculated; According to all the actual power consumption, total power consumption of the whole machine in the current flight state is calculated. 6.The UAV remaining time of flight prediction method of claim 1, wherein, The total power consumption is corrected according to the battery aging correction coefficient and the wind speed correction coefficient to obtain a corrected average power consumption for endurance evaluation, including: The total power consumption is preliminarily corrected according to the battery aging correction coefficient to obtain a first corrected power consumption; When it is determined according to the operating environment data that the current wind speed meets the wind speed correction condition, the first corrected power consumption is corrected according to the wind speed correction coefficient to obtain a final power consumption value; According to the on-board device fixed power consumption and the final power consumption, real-time total power consumption of the whole machine is calculated; Real-time total power consumption of the whole machine collected in a preset time period is acquired; The real-time total power consumption of the whole machine is weighted and averaged to obtain an average power consumption value; When the current flight speed of the unmanned aerial vehicle exceeds a preset speed threshold, an included angle relationship between the current wind speed and the current heading is acquired; The average power consumption value is corrected according to the included angle relationship to obtain a second corrected power consumption; The second corrected power consumption is corrected according to the battery aging correction coefficient to obtain a final corrected average power consumption for endurance evaluation. 7.The UAV remaining time of flight prediction method of claim 1, wherein, After the theoretical remaining flight time in the current flight state without considering the return constraint is calculated according to the corrected average power consumption, a preset flight parameter and the effective remaining electric quantity, including: According to the unmanned aerial vehicle itself parameter, a preset flight parameter and the unmanned aerial vehicle flight data, total energy necessary for safe return is calculated; According to the effective remaining electric quantity and the total energy necessary for safe return, a target remaining electric quantity after completing safe return is calculated; According to the target remaining electric quantity and the corrected average power consumption, a safety time margin available for continuing to perform the current task or returning calmly is calculated. 8.The UAV remaining time of flight prediction method of claim 7, wherein, The total energy necessary for safe return is calculated according to the unmanned aerial vehicle itself parameter, a preset flight parameter and the unmanned aerial vehicle flight data, including: According to the unmanned aerial vehicle itself parameter, the total mass of the unmanned aerial vehicle is acquired, and according to the unmanned aerial vehicle flight data, the return cruise speed, the cruise power consumption, the hovering power consumption, the return horizontal distance from the current position to the return point and the return vertical height difference are acquired, and according to the preset flight parameter, the vertical climbing efficiency is acquired; According to the total mass of the unmanned aerial vehicle, the return vertical height difference and the vertical climbing efficiency, the energy required in the return climbing stage is calculated; According to the return horizontal distance, the return cruise speed and the cruise power consumption, the energy required in the cruise stage is calculated; According to the energy required in the return climbing stage, the energy required in the cruise stage and the hovering power consumption, the total energy required for safe return is calculated. 9.The UAV remaining time of flight prediction method of claim 7, wherein, The method further comprises: output the theoretical remaining flight time and the safety time margin; When the safety time margin is not greater than a preset time threshold, it is determined that the current power of the unmanned aerial vehicle is insufficient, and an alarm prompt information that the insufficient power needs to start an emergency return program or execute a forced landing plan is output.

10. An unmanned aerial vehicle endurance prediction device, comprising: The unmanned aerial vehicle remaining flight time prediction device comprises: A first acquisition unit is configured to acquire the unmanned aerial vehicle device state, the operating environment data and the unmanned aerial vehicle flight data in real time when the unmanned aerial vehicle is running; A second acquisition unit is configured to acquire the current air density and the wind speed correction coefficient according to the operating environment data; A third acquisition unit is configured to acquire the battery aging correction coefficient and the battery temperature correction coefficient according to the unmanned aerial vehicle device state; A first calculation unit is configured to calculate the total power consumption of the whole machine in the current flight state according to the current air density, the unmanned aerial vehicle device state, the unmanned aerial vehicle flight data, the preset unmanned aerial vehicle itself parameter, the preset on-board device fixed power consumption and the preset efficiency mapping table; A correction unit is configured to correct the total power consumption according to the battery aging correction coefficient and the wind speed correction coefficient to obtain the corrected average power consumption for endurance evaluation; A second calculation unit is configured to calculate the current effective remaining power according to the battery temperature correction coefficient and the current battery remaining power of the unmanned aerial vehicle; A third calculation unit is configured to calculate the theoretical remaining flight time in the current flight state without considering the return constraint according to the corrected average power consumption, the preset flight parameter and the effective remaining power.

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