Unmanned aerial vehicle power control system based on fault detection

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle power control system based on fault detection. The system collects state data of each component of the unmanned aerial vehicle in real time, the data calculation module accurately calculates key parameters such as battery remaining capacity and energy, fault states such as over-charging, over-discharging and temperature abnormity of the battery are rapidly judged, and meanwhile, the rotating speed and the load condition of the motor are monitored; the power management module dynamically calculates residual flight time and an optimal power distribution strategy according to data monitored in real time, power output can be rapidly adjusted when abnormal conditions are detected, and the feedback control module controls the power distribution strategy according to the flight attitude angle of the unmanned aerial vehicle. The power output of the motor is adjusted in real time, the power management and fault detection capability of the unmanned aerial vehicle in a complex environment is remarkably improved, the problems of over-charging, over-discharging and the like of the battery are effectively avoided, and the safety and reliability of the unmanned aerial vehicle are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a power control system of an unmanned aerial vehicle based on fault detection. Background Art

[0002] With the rapid development of UAV technology, UAVs are increasingly used in military, agriculture, logistics, surveying and mapping and other fields. As its core component, the UAV power control system not only directly affects the flight safety and stability of the UAV, but also its endurance and mission execution efficiency. Through effective power management, UAVs can monitor and optimize battery usage in real time to ensure efficient flight in complex environments, while reducing energy consumption and extending flight time. In addition, advanced power control technology can detect and handle potential faults in a timely manner, improve the reliability and safety of UAVs, and enable them to perform better in critical tasks.

[0003] Traditional power management systems are difficult to respond quickly to complex and changing flight environments, which can easily lead to problems such as battery overcharging and over-discharging, thus affecting the normal operation of the drone. At the same time, the existing system has limited fault detection capabilities and cannot fully monitor the status of each component, especially in various abnormal situations, which may cause the system to be unable to take timely countermeasures. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a UAV power control system based on fault detection. By real-time collection of status data of various components of the UAV, including battery voltage, current, temperature and motor operation, the status of the UAV is fully monitored. The data calculation module accurately calculates key parameters such as the remaining battery power and energy, and promptly provides them to the fault detection module, which can quickly determine the fault states of the battery such as overcharge, over-discharge and abnormal temperature, and monitors the speed and load of the motor at the same time, ensuring that the system can still take countermeasures in time under various abnormal conditions. The power management module dynamically calculates the remaining flight time and the optimal power allocation strategy based on the real-time monitoring data to ensure the power utilization efficiency of the UAV in the flight mission. When an abnormal situation is detected, the power output can be quickly adjusted, and the power consumption can be limited through the emergency power use strategy to prevent battery damage and system failure. The feedback control module adjusts the motor power output in real time according to the flight attitude angle of the UAV, thereby stably controlling the flight attitude of the UAV. The system significantly improves the power management and fault detection capabilities of the UAV in a complex environment, not only effectively avoiding problems such as battery overcharge and over-discharge, but also enhancing the safety and reliability of the UAV, ensuring its normal operation in various flight missions, and solving the above problems.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a UAV power control system based on fault detection, comprising a data acquisition module, a data calculation module, a fault detection module, a power management module and a feedback control module;

[0006] The data acquisition module is used to collect status data of various components of the drone and transmit it to the data calculation module. The status data of various components of the drone include battery voltage, battery current, battery temperature, motor speed, motor load, drone height, drone speed, drone angle, drone flight environment temperature, drone flight environment humidity and drone flight air pressure;

[0007] The data calculation module calculates the remaining battery power of the drone, the battery energy of the drone, the motor efficiency of the drone, the pitch angle of the drone, the roll angle of the drone and the heading angle of the drone according to the status data of each component of the drone transmitted by the data acquisition module, and sends the calculation to the fault detection module after completion;

[0008] The fault detection module monitors the battery status of the drone according to the calculation parameters transmitted by the data calculation module, determines whether the drone is overcharged, over-discharged, or has abnormal temperature, and performs motor fault diagnosis on the drone to monitor whether the speed is running normally and whether the load is abnormal. After the detection is completed, a detection signal is generated and sent to the power management module;

[0009] The power management module calculates the remaining flight time of the drone based on the remaining battery power and the battery energy of the drone, calculates the optimal power allocation strategy based on the flight mission of the day, and calculates the emergency power usage strategy to limit power output when the transmitted detection signal shows an abnormality;

[0010] The feedback control module adjusts the motor power output of the drone according to the optimal power distribution strategy and the emergency power usage strategy, and controls the flight attitude of the drone according to the drone flight pitch angle, drone flight roll angle and drone flight heading angle, so as to stably control the flight of the drone.

[0011] Preferably, the calculation formula for the remaining battery power of the drone is as follows:

[0012]

[0013] In the formula, Indicates the remaining battery power of the drone. Indicates at time The battery current at the moment, Indicates the time when the battery starts to discharge. Represents integral.

[0014] Preferably, the calculation formula of the drone battery energy is as follows:

[0015]

[0016] In the formula, Indicates the drone battery energy, Indicates the drone battery capacity, Indicates the voltage of the drone battery.

[0017] Preferably, the calculation formula of the motor efficiency of the drone is as follows:

[0018]

[0019] In the formula, represents the motor efficiency of the drone, Indicates the motor output power, Indicates the motor input power.

[0020] Preferably, the calculation formula for the UAV flight pitch angle is as follows:

[0021]

[0022] In the formula, Indicates the pitch angle of the drone flight. Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the inverse tangent function.

[0023] Preferably, the calculation formula for the UAV flight roll angle is as follows:

[0024]

[0025] In the formula, Indicates the UAV flight roll angle, Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the inverse tangent function.

[0026] Preferably, the calculation formula for the UAV flight heading angle is as follows:

[0027]

[0028] In the formula, Indicates the flight heading angle of the drone. The magnetometer measures Axis magnetic field strength, The magnetometer measures Axis magnetic field strength, Indicates the magnetic field offset compensation of the drone's flight area.

[0029] Preferably, the calculation formula for the remaining flight time of the drone is as follows:

[0030]

[0031] In the formula, Indicates the remaining flight time of the drone. Indicates the remaining battery power of the drone. Indicates the drone battery energy.

[0032] Preferably, the optimal power distribution strategy calculation formula is as follows:

[0033]

[0034] In the formula, represents the optimal power distribution, Indicates the total available power of the drone, Indicates the remaining battery power of the drone. Indicates the total battery energy.

[0035] Preferably, the calculation formula for the emergency power usage strategy is as follows:

[0036]

[0037] In the formula, Indicates emergency power use, Indicates the current UAV power output. Indicates the UAV power adjustment coefficient in emergency status.

[0038] Compared with the prior art, the present invention provides a UAV power control system based on fault detection, which has the following beneficial effects:

[0039] The present invention ensures comprehensive monitoring of the status of the drone by collecting status data of various components of the drone in real time, including battery voltage, current, temperature and motor operation. The data calculation module accurately calculates key parameters such as the remaining battery power and energy, and promptly provides them to the fault detection module, which can quickly determine the fault states of the battery such as overcharge, over-discharge and temperature abnormality, and monitors the speed and load of the motor at the same time, ensuring that the system can still take countermeasures in time under various abnormal conditions. The power management module dynamically calculates the remaining flight time and the optimal power allocation strategy based on the real-time monitoring data to ensure the power utilization efficiency of the drone in the flight mission. When an abnormal situation is detected, the power output can be quickly adjusted, and the power consumption can be limited through the emergency power use strategy to prevent battery damage and system failure. The feedback control module adjusts the motor power output in real time according to the flight attitude angle of the drone, thereby stably controlling the flight attitude of the drone, significantly improving the power management and fault detection capabilities of the drone in a complex environment, not only effectively avoiding problems such as battery overcharge and over-discharge, but also enhancing the safety and reliability of the drone, ensuring its normal operation in various flight missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The traditional power management system is difficult to respond quickly to the complex and changing flight environment, which easily leads to problems such as battery overcharging and over-discharging, thus affecting the normal operation of the UAV. At the same time, the existing system has limited fault detection capabilities and cannot fully monitor the status of each component, especially in a variety of abnormal situations, which may cause the system to be unable to take timely countermeasures. Therefore, a UAV power control system based on fault detection is proposed. Please refer to Figure 1 ,The system includes a data acquisition module, a data calculation module, a fault detection module, a power management module and a feedback control module;

[0043] The data acquisition module monitors the status of each component of the drone in real time through sensitive sensors and efficient data communication interfaces to ensure comprehensive and accurate collection of key operating data. The module uses high-precision voltage sensors and current sensors to measure battery voltage and current to ensure accurate evaluation of battery status. At the same time, it monitors the operating temperature of the battery and motor through temperature sensors to avoid performance degradation and potential failures caused by overheating. The motor speed sensor can provide real-time feedback on the working status of the motor, and monitor the load changes on the motor through the load sensor, thereby providing real-time monitoring of the motor performance. The module also integrates altitude sensors, speed sensors and air pressure sensors to obtain the flight altitude, speed and air pressure data of the drone, thereby reflecting its dynamic changes in different flight environments. The angle of the drone (including pitch angle, roll angle and heading angle) is calculated by the inertial measurement unit (IMU) to ensure the accuracy of the aircraft attitude control. In terms of environmental data, the module also monitors the temperature and humidity of the flight environment, which is crucial to the performance and safety of the drone. All collected data is transmitted to the data calculation module in the form of digital signals through a high-speed data bus, providing a solid foundation for subsequent data analysis and processing, thereby realizing efficient interconnection and collaborative work of drone power management and fault detection.

[0044] The data calculation module calculates the remaining battery power of the drone, the battery energy of the drone, the motor efficiency of the drone, the pitch angle of the drone, the roll angle of the drone and the heading angle of the drone according to the status data of each component of the drone transmitted by the data acquisition module, where:

[0045] The formula for calculating the remaining battery power of the drone is as follows:

[0046]

[0047] Calculating the remaining power of the drone battery can monitor the health of the battery in real time to ensure that the battery will not run out during flight. This real-time feedback allows the operator to clearly understand the battery usage and make reasonable flight decisions. For example, in long-term flight missions, the operator can adjust the flight route according to the remaining power and choose a safer return path to avoid accidental landings due to insufficient power. In the formula, Indicates the remaining battery power of the drone. Indicates at time The battery current at the moment, Indicates the time when the battery starts to discharge. Accurate remaining power monitoring can also prevent overcharging and over-discharging of the battery, both of which will significantly shorten the battery life. By effectively managing the battery status, the reliability and safety of the entire drone system are improved;

[0048] The formula for calculating the drone battery energy is as follows:

[0049]

[0050] By calculating the energy of the drone battery, the system can more comprehensively evaluate the actual battery usage capacity. Battery energy is not just a simple calculation of the remaining power, but also involves a comprehensive analysis of multiple factors such as the battery voltage and current. Knowing the state of battery energy, the operator can better plan the flight mission and ensure that there is enough energy to support the normal operation of the drone when performing the mission. In the formula, Indicates the drone battery energy, Indicates the drone battery capacity, Indicates the voltage of the drone battery. Accurate battery energy calculation improves the operating efficiency of the drone and the reliability of mission completion. It also helps optimize the battery charging process to ensure that overcharging does not occur during charging, thereby extending the battery life.

[0051] The formula for calculating the motor efficiency of the drone is as follows:

[0052]

[0053] By monitoring the efficiency of the motor in real time, the system can identify the working status of the motor under different loads and determine whether it is operating within the optimal efficiency range. Improving the efficiency of the motor can not only reduce energy consumption, but also reduce heat and wear, thereby extending the service life of the motor. In the formula, represents the motor efficiency of the drone, Indicates the motor output power, Represents the motor input power. Understanding the motor efficiency can also help optimize the flight mode and mission execution strategy, ensuring that the UAV can complete its goals with the lowest energy consumption when performing complex tasks. This efficient motor management improves the overall performance of the UAV and enhances its adaptability in a variety of application scenarios.

[0054] The calculation formula of the UAV flight pitch angle is as follows:

[0055]

[0056] By monitoring the pitch angle in real time, the system can adjust the attitude of the drone in time to ensure its stability during flight. This control capability is crucial for dealing with external factors such as sudden airflow changes and wind speed fluctuations, and can effectively reduce the risk of loss of control during flight. In the formula, Indicates the pitch angle of the drone flight. Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the arc tangent function. The calculation of the pitch angle can also help the UAV maintain the best posture in different flight phases (such as take-off, cruising, and landing), optimize flight efficiency, and reduce energy consumption;

[0057] The calculation formula of the UAV flight roll angle is as follows:

[0058]

[0059] By monitoring the roll angle in real time, the system can ensure that the drone maintains a horizontal or specific flight attitude during flight, avoiding unstable flight caused by excessive tilt. This control capability is essential for performing complex flight missions (such as aerial photography, patrolling, etc.) because it can ensure the stable flight of the drone under various environmental conditions. In the formula, Indicates the UAV flight roll angle, Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the arctangent function. Monitoring the roll angle can also help the UAV maintain flexibility during maneuvering flight, ensure rapid response to changes in the external environment, and improve flight safety and efficiency;

[0060] The calculation formula of the UAV flight heading angle is as follows:

[0061]

[0062] By real-time monitoring of the heading angle, the system can adjust the flight direction of the drone in time to avoid deviation from the route and collision risks. This is especially important for performing precise tasks (such as terrain surveys, material delivery, etc.) and can improve the success rate of the mission. In the formula, Indicates the flight heading angle of the drone. The magnetometer measures Axis magnetic field strength, The magnetometer measures Axis magnetic field strength, Indicates the magnetic field offset compensation of the drone's flight area. The monitoring of the heading angle can also help the drone fly more flexibly in complex environments (such as cities or mountainous areas) and ensure safe flight under various obstacles and weather conditions;

[0063] The fault detection module is equipped with high-precision voltage and current sensors, which can monitor the voltage and current data of the battery in real time to accurately identify overcharge and over-discharge conditions and ensure that the battery operates within a safe operating range. It is also equipped with a temperature sensor to monitor the temperature changes of the battery and motor, and detect temperature abnormalities in time, thereby preventing faults caused by overheating or overcooling.

[0064] In terms of motor fault diagnosis, the fault detection module uses speed sensors and load sensors to monitor the motor speed and load status in real time. These sensors provide instant feedback on the motor operation through high-frequency data sampling and signal processing to ensure that it operates normally according to the design standards. The system monitors whether the speed is within the normal range by setting reasonable thresholds, and uses dynamic load analysis algorithms to determine whether the load status is abnormal (such as overload, flameout, etc.), so as to identify potential faults in a timely manner;

[0065] After completing all monitoring, the fault detection module will integrate various monitoring data to generate detailed detection signals. These signals not only include real-time status and detection results, but also include risk assessment of potential faults and corresponding maintenance suggestions. Finally, this information will be transmitted to the power management module in real time to assist it in optimizing the power distribution strategy and ensure that the drone can maintain the optimal operating state at all flight stages.

[0066] The power management module can accurately calculate the remaining flight time of the drone by comprehensively analyzing the remaining power and battery energy of the drone battery. This process involves real-time monitoring of battery parameters such as voltage, current and temperature to ensure the accuracy and reliability of the calculation. Through in-depth analysis of the battery status, the power management module can dynamically adjust the power usage strategy according to the flight mission requirements of the day to ensure that the drone has sufficient power support when performing the mission;

[0067] When calculating the optimal power distribution strategy, the module takes into account multiple factors, including flight altitude, speed, route, and environmental conditions. This intelligent algorithm can reasonably distribute power at different flight stages to ensure that each system (such as navigation, communication, and sensors) operates in the best working state, thereby improving the overall performance and flight efficiency of the drone. For example, when performing high-load tasks, the system will give priority to ensuring the power supply of key equipment, and will appropriately reduce power consumption during cruising or standby stages to extend flight time.

[0068] When receiving an abnormal signal from the fault detection module, the power management module will quickly start the emergency power usage strategy. This strategy ensures that the key systems of the drone (such as flight control and communication systems) can continue to work normally by limiting power output, while reducing the energy consumption of non-critical systems to cope with emergencies. At this time, the system may temporarily shut down certain auxiliary equipment or reduce the power output of the motor to extend the drone's endurance and ensure its safe return or completion of emergency tasks. Through this comprehensive power management strategy, the power management module not only optimizes the power usage efficiency of the drone, but also greatly improves its adaptability and safety in complex environments;

[0069] The calculation formula for the remaining flight time of the drone is as follows:

[0070]

[0071] By calculating the remaining flight time of the drone in real time, the operator can make reasonable adjustments based on the current battery status and flight plan. This information can help the operator plan the flight route reasonably in complex tasks and ensure that the drone returns safely or completes the mission before the battery runs out. In the formula, Indicates the remaining flight time of the drone. Indicates the remaining battery power of the drone. Indicates the battery energy of the drone. Knowing the remaining flight time can also optimize the scheduling of tasks and resource allocation, and improve the efficiency and flexibility of drones in various application scenarios. This function is particularly important in long-term flight missions because it can effectively reduce the flight risks caused by insufficient power.

[0072] The optimal power distribution strategy calculation formula is as follows:

[0073]

[0074] By analyzing the current battery status, flight mission requirements and environmental factors, the system can develop the best power distribution plan to ensure that each component operates in the best working condition. This optimization can not only extend the flight time, but also reduce energy consumption and improve the overall performance of the drone. In the formula, represents the optimal power distribution, Indicates the total available power of the drone, Indicates the remaining battery power of the drone. Represents the total battery energy. A reasonable power distribution strategy can also improve the adaptability of the drone in various mission scenarios and ensure its efficient operation in complex environments.

[0075] The calculation formula for emergency power usage strategy is as follows:

[0076]

[0077] The calculation of the emergency power usage strategy provides the UAV with the ability to respond to emergencies. When the system detects an abnormal situation (such as battery overheating, load abnormality, etc.), it can quickly adjust the power output, limit unnecessary energy consumption, and ensure the normal operation of key systems (such as navigation and communication). In the formula, Indicates emergency power use, Indicates the current drone power output. Indicates the UAV power adjustment coefficient in emergency situations. Through timely emergency response, the UAV can maintain a stable flight state in a complex and uncertain environment to ensure the safe completion of the mission.

[0078] The feedback control module accurately adjusts the power output of the motor by real-time analysis of the optimal power distribution strategy and emergency power use strategy, thereby achieving fine control of the UAV flight. This module uses an efficient adaptive control algorithm to process input data from the data calculation module and the fault detection module. According to the current battery status and remaining power, it can intelligently adjust the power of the motor to ensure that the UAV maintains optimal energy efficiency and power output in different flight stages (such as takeoff, cruising and landing);

[0079] The feedback control module also relies on a variety of sensors (accelerometers, gyroscopes and magnetometers) to monitor the key flight attitude parameters of the drone, such as pitch angle, roll angle and heading angle, in real time. The real-time data provided by these sensors enables the system to accurately determine the current attitude and motion state of the drone. When the flight attitude deviates, the feedback control module will quickly calculate the required adjustment angle and adjust the power output of the motor through the control algorithm to achieve stable attitude control. For example, when the drone implements pitch changes, the control module will adjust the power distribution of the front and rear motors to achieve upward or downward pitch. When rolling or heading adjustments are made, the power of the lateral motor will be adjusted to maintain flight balance.

[0080] This real-time feedback mechanism ensures the stability and accuracy of the drone in complex environments, enabling it to cope with various flight challenges, including wind speed changes, load fluctuations and sudden failures. By continuously monitoring and adjusting the flight attitude, it significantly improves the safety and reliability of the drone when performing various tasks (such as aerial photography, surveying and delivery), enabling it to fly autonomously in various environmental conditions.

[0081] Through the comprehensive application of the above systems, the power management and fault detection capabilities of drones in complex environments have been significantly improved. Not only have problems such as battery overcharging and over-discharging been effectively avoided, but the safety and reliability of drones have also been enhanced, ensuring their normal operation in various flight missions.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV power control system based on fault detection, characterized in that: It includes a data acquisition module, a data calculation module, a fault detection module, a power management module and a feedback control module; The data acquisition module is used to collect status data of various components of the drone and transmit it to the data calculation module. The status data of various components of the drone include battery voltage, battery current, battery temperature, motor speed, motor load, drone height, drone speed, drone angle, drone flight environment temperature, drone flight environment humidity and drone flight air pressure; The data calculation module calculates the remaining battery power of the drone, the battery energy of the drone, the motor efficiency of the drone, the pitch angle of the drone, the roll angle of the drone and the heading angle of the drone according to the status data of each component of the drone transmitted by the data acquisition module, and sends the calculation to the fault detection module after completion; The fault detection module monitors the battery status of the drone according to the calculation parameters transmitted by the data calculation module, determines whether the drone is overcharged, over-discharged, or has abnormal temperature, and performs motor fault diagnosis on the drone to monitor whether the speed is running normally and whether the load is abnormal. After the detection is completed, a detection signal is generated and sent to the power management module; The power management module calculates the remaining flight time of the drone based on the remaining battery power and the battery energy of the drone, calculates the optimal power allocation strategy based on the flight mission of the day, and calculates the emergency power usage strategy to limit power output when the transmitted detection signal shows an abnormality; The feedback control module adjusts the motor power output of the drone according to the optimal power distribution strategy and the emergency power usage strategy, and controls the flight attitude of the drone according to the drone flight pitch angle, drone flight roll angle and drone flight heading angle, so as to stably control the flight of the drone.

2. The UAV power control system based on fault detection according to claim 1, characterized in that: The calculation formula for the remaining battery power of the drone is as follows: , In the formula, Indicates the remaining battery power of the drone. Indicates at time The battery current at the moment, Indicates the time when the battery starts to discharge. Represents integral.

3. The UAV power control system based on fault detection according to claim 2 is characterized in that: The calculation formula of the drone battery energy is as follows: , In the formula, Indicates the drone battery energy, Indicates the drone battery capacity, Indicates the voltage of the drone battery.

4. The UAV power control system based on fault detection according to claim 3 is characterized in that: The calculation formula of the motor efficiency of the UAV is as follows: , In the formula, represents the motor efficiency of the drone, Indicates the motor output power, Indicates the motor input power.

5. The UAV power control system based on fault detection according to claim 4 is characterized in that: The calculation formula of the UAV flight pitch angle is as follows: , In the formula, Indicates the pitch angle of the drone flight. Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the inverse tangent function.

6. The UAV power control system based on fault detection according to claim 5 is characterized in that: The calculation formula of the UAV flight roll angle is as follows: , In the formula, Indicates the UAV flight roll angle, Indicates that the drone is The speed in the axis direction, Indicates that the drone is The speed in the axis direction, Represents the inverse tangent function.

7. The UAV power control system based on fault detection according to claim 6 is characterized by: The calculation formula of the UAV flight heading angle is as follows: , In the formula, Indicates the flight heading angle of the drone. The magnetometer measures Axis magnetic field strength, The magnetometer measures Axis magnetic field strength, Indicates the magnetic field offset compensation of the drone's flight area.

8. The UAV power control system based on fault detection according to claim 7 is characterized in that: The calculation formula for the remaining flight time of the drone is as follows: , In the formula, Indicates the remaining flight time of the drone. Indicates the remaining battery power of the drone. Indicates the drone battery energy.

9. The UAV power control system based on fault detection according to claim 8, characterized in that: The optimal power distribution strategy calculation formula is as follows: , In the formula, represents the optimal power distribution, Indicates the total available power of the drone, Indicates the remaining battery power of the drone. Indicates the total battery energy.

10. The UAV power control system based on fault detection according to claim 9, characterized in that: The calculation formula of the emergency power usage strategy is as follows: , In the formula, Indicates emergency power use, Indicates the current drone power output. Indicates the UAV power adjustment coefficient in emergency status.

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