An unmanned aerial vehicle flight control monitoring integrated system for power transmission tower inspection
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIAMUSI POWER IND BUREAU
- Filing Date
- 2025-05-15
- Publication Date
- 2026-06-02
Smart Images
Figure CN120215555B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of unmanned aerial vehicle (UAV) flight control technology, specifically to an integrated UAV flight control and monitoring system for power transmission tower inspection. Background Technology
[0002] Currently, the fittings on power transmission towers are prone to wear and overheating. At present, multi-rotor drones are commonly used for intelligent inspection of power transmission towers. The images of power transmission towers captured by multi-rotor drones are used to monitor for faults. Since the quality of the images of power transmission towers directly affects the accuracy of fault monitoring, the flight stability of multi-rotor drones is very important.
[0003] Existing technologies mostly use quadcopter drones for intelligent inspection of power transmission towers. However, due to the influence of wind in the environment, different rotors are disturbed to varying degrees during flight, which may cause the drone fuselage to tilt excessively. This results in poor stability of the drone fuselage in harsh environments, affecting the clarity of the images taken of the power transmission towers. Summary of the Invention
[0004] In view of the above, it is necessary to provide an integrated UAV flight control and monitoring system for power transmission tower inspection. Compared with the traditional UAV flight control and monitoring system, this system improves the stability of the UAV during flight by correcting its flight attitude, thereby improving the efficiency and quality of power transmission tower inspection.
[0005] The UAV flight control and monitoring integrated system for power transmission tower inspection proposed in this application adopts the following technical solution:
[0006] One embodiment of this application provides an integrated UAV flight control and monitoring system for power transmission tower inspection, the system comprising:
[0007] The flight monitoring module is used to acquire the wind speed, rotation speed of each rotor of the drone, and tilt angle of the drone fuselage in real time.
[0008] The flight control module is used to obtain the disturbance value of each rotor in each preset time period by analyzing the correlation between wind speed and the rotation speed of each rotor in each preset time period, as well as the dispersion of the rotation speed of each rotor.
[0009] By analyzing the changes in the tilt angle within each preset time period, the tilt fluctuation of the drone body within each preset time period is obtained.
[0010] By analyzing the correlation between the disturbance value and the tilt fluctuation, and combining the growth of the disturbance value within all the preset time periods, the disturbance factor of each rotor position at the current moment is obtained;
[0011] Based on the distribution of the disturbance factors and the preset maximum motor power of the brushless motors in each rotor, the power of each brushless motor in the rotor is adjusted at the current moment.
[0012] The obstacle avoidance module is used to plan flight paths to avoid obstacles;
[0013] The tower monitoring module is used to monitor damage information of transmission towers;
[0014] A wireless communication module is used to transmit the damage information to a ground station.
[0015] In one embodiment, the process of obtaining the disturbance value is as follows:
[0016] The wind speeds within each preset time period are arranged in chronological order to form wind speed sequences.
[0017] The rotational speeds of each rotor within each preset time period are arranged in chronological order to form a sequence of rotational speeds for each preset time period.
[0018] Calculate the grey correlation coefficient between the wind speed sequence and the rotational speed sequence for each preset time period;
[0019] The disturbance value is obtained by the grey relational coefficient and the dispersion.
[0020] In one embodiment, the perturbation value is the product of the gray relational coefficient and the dispersion.
[0021] In one embodiment, the process of obtaining the tilt fluctuation is as follows:
[0022] The tilt angles within each preset time period are arranged in chronological order to form a sequence of tilt angles;
[0023] The tilt volatility is the mean of the absolute values of all elements in the first-order difference sequence of the corresponding tilt angle sequence.
[0024] In one embodiment, the process of obtaining the disturbance factor is as follows:
[0025] The disturbance values of each rotor within all the preset time periods are arranged in chronological order to form the disturbance sequence of each rotor.
[0026] Arrange the tilt fluctuations of the drone body within all the preset time periods in chronological order to form a tilt fluctuation sequence of the drone body;
[0027] Calculate the correlation between each disturbance sequence and the tilted fluctuation sequence;
[0028] Calculate the sum of all positive numbers in the first-order difference sequence of each perturbation sequence;
[0029] By combining the correlation degree and the sum, the disturbance factor of each rotor position at the current moment is obtained.
[0030] In one embodiment, the perturbation factor is the product of the correlation degree and the sum.
[0031] In one embodiment, adjusting the power of the brushless motors within each rotor blade at the current moment includes:
[0032] Calculate the average of the normalized values of the disturbance factors for all rotor positions at the current moment;
[0033] By comparing the normalized value of the disturbance factor at each rotor position at the current moment with the average value, and the difference between the maximum motor power and the power of the brushless motor in each rotor at the current moment, the motor adjustment power of the brushless motor in each rotor at the current moment is obtained.
[0034] The power of the brushless motors in each rotor blade is adjusted by regulating the power of the motor.
[0035] In one embodiment, the process of obtaining the motor adjustment power is as follows:
[0036] The difference between the normalized value of the disturbance factor at each rotor position at the current moment and the average value is denoted as the first difference.
[0037] The difference between the maximum motor power and the power of the brushless motor in each rotor at the current moment is recorded as the second difference.
[0038] By combining the power of the brushless motors in each rotor blade at the current moment with the first difference and the second difference, the motor adjustment power of the brushless motors in each rotor blade at the current moment can be obtained.
[0039] In one embodiment, the calculation process for the motor adjustment power is as follows:
[0040] Calculate the product of the first difference and the second difference, whereby the motor adjustment power is the sum of the power of the brushless motors in each rotating blade at the current moment and the product.
[0041] In one embodiment, adjusting the power of the brushless motors in each rotor at the current moment includes:
[0042] The power of the brushless motors in each rotor at the current moment, along with the motor adjustment power, is input into the flight controller in the flight control module. The flight controller outputs control signals and transmits them to each electronic regulator. Each electronic regulator adjusts the power of each brushless motor through current transmission.
[0043] This application has at least the following beneficial effects:
[0044] This application measures the wind disturbance characteristics and tilt fluctuation characteristics of each rotor during the flight of a quadcopter drone by collecting tilt angle, wind speed, and rotation speed. It also considers both wind disturbance characteristics and tilt fluctuation characteristics to comprehensively analyze and measure the lift suppression effect at each rotor position during the drone's flight. Furthermore, it utilizes the differences in lift suppression effects between rotor positions to precisely control and adjust the brushless motor, thereby improving the accuracy of correcting the drone's flight attitude.
[0045] This application compensates for the impact of wind speed by accurately controlling and adjusting the power of the brushless motors in each rotor, ensuring the stability of the quadcopter drone when flying in harsh environments, improving the clarity of the images taken of power transmission towers, and thus improving the quality and efficiency of power transmission tower inspections.
[0046] This application identifies obstacles around the quadcopter drone and the distance between the drone and the obstacles through a flight obstacle avoidance module, plans a safe flight path in advance, avoids collisions with obstacles, and improves the safety of the quadcopter drone during flight. Attached Figure Description
[0047] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 A block diagram of an integrated UAV flight control and monitoring system for power transmission tower inspection provided in this application;
[0049] Figure 2 A schematic diagram illustrating the process of obtaining the motor's adjustable power;
[0050] Figure 3 This is a flowchart of the power regulation process. Detailed Implementation
[0051] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.
[0052] 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 belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".
[0053] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.
[0054] The following description, in conjunction with the accompanying drawings, details a specific solution for an integrated UAV flight control and monitoring system for power transmission tower inspection provided in this application.
[0055] Please see Figure 1 The diagram illustrates a block diagram of an integrated UAV flight control and monitoring system for power transmission tower inspection, provided in one embodiment of this application. The system includes: a flight monitoring module 101, a flight control module 102, a flight obstacle avoidance module 103, a tower monitoring module 104, and a wireless communication module 105.
[0056] The flight monitoring module 101 is used to acquire in real time the wind speed in the environment where the UAV is located, the rotation speed of each rotor of the UAV, and the tilt angle of the UAV fuselage.
[0057] The flight detection module uses an inclination sensor inside the quadcopter to collect the tilt angle of the drone's fuselage relative to the horizontal plane in real time, an inclination sensor inside the quadcopter to collect the wind speed in the environment where the drone is located in real time, and an inclination sensor inside the quadcopter to collect the rotation speed of the four rotors of the drone in real time during the inspection process.
[0058] In this embodiment, the acquisition frequency of tilt angle, wind speed and rotation speed is 100Hz. The acquisition frequency value is preset by the user and can be set by the implementer. This application does not impose any special restrictions.
[0059] The flight control module 102 is used to acquire the disturbance value of each rotor within each preset time period; acquire the tilt fluctuation of the UAV fuselage within each preset time period by analyzing the change of tilt angle within each preset time period; acquire the disturbance factor of each rotor position at the current moment by analyzing the correlation between the disturbance value and the tilt fluctuation, and combining the growth of the disturbance value within all preset time periods; and adjust the power of the brushless motor in each rotor at the current moment by the distribution of the disturbance factor and the preset maximum motor power of the brushless motor in each rotor.
[0060] During the inspection of power transmission towers using quadcopter drones, the different rotors are affected by wind forces to varying degrees during flight. This is especially true in harsh environments, where the degree of wind disturbance varies significantly among different rotors, resulting in poor stability of the quadcopter and affecting the clarity of the images captured on the power transmission towers. Therefore, to ensure the stability of the quadcopter drone during tower inspections and thus improve the quality of the images, it is necessary to control the attitude of the quadcopter drone during the inspection process.
[0061] Under the influence of strong winds at high altitudes, the four rotors of the UAV are affected by wind disturbances to varying degrees. In order to analyze the wind disturbance characteristics of each rotor of the UAV, the wind speed and tilt angle per second in the minute before the current moment are arranged in chronological order to form a wind speed sequence and tilt angle sequence per second. The rotation speed of each rotor per second in the minute before the current moment is arranged in chronological order to form a rotation speed sequence per second.
[0062] It should be noted that the minute mentioned is adjacent to the current time. The minute and the second are only one embodiment of this application, and the implementer can set their specific values as they see fit.
[0063] The grey relational analysis (GRA) algorithm is used to obtain the grey relational coefficient between the rotational speed sequence and the wind speed sequence. The wind speed sequence per second in the minute before the current time is taken as the parent sequence, and the rotational speed sequence per second in the minute before the current time is taken as the child sequence. The parent sequence and the child sequence are input into the grey relational analysis algorithm, and the grey relational coefficient between the wind speed sequence per second in the minute before the current time and the rotational speed sequence is output. The grey relational analysis algorithm is a well-known technology and will not be described in detail in this application.
[0064] The larger the grey correlation coefficient between the wind speed sequence and the rotation speed sequence, the stronger the correlation between the rotation speed of the drone's rotor and the wind speed in the environment. In this case, the rotation speed of the rotor is more affected by the environmental wind speed. If the dispersion of the rotation speed of the drone's rotor is higher, it will highlight the influence of the environmental wind force on the drone, which will greatly disturb the stability of the rotor speed, affect the flight stability of the quadcopter drone, and reduce the efficiency and quality of power transmission tower inspection.
[0065] Based on the above analysis, the dispersion of the rotational speed of each rotor within one minute before the current moment is obtained. The product of the dispersion and the corresponding gray correlation coefficient is used as the disturbance value of each rotor within one minute before the current moment.
[0066] In this embodiment, the dispersion is the coefficient of variation, which is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to measure the unevenness of the distribution of rotor speed, implementers may use other existing techniques for measurement, such as variance, standard deviation, etc. This application does not impose any special restrictions.
[0067] It should be noted that the disturbance value reflects the wind disturbance characteristics of each rotor when the quadcopter drone inspects the power transmission tower at high altitude. If the wind disturbance of the rotor is greater in the minute before the current moment, it indicates that the wind force has a stronger inhibitory effect on the rotor rotation, and it is less likely to ensure the stability of the drone body when inspecting the power transmission tower.
[0068] Furthermore, the first-order difference sequence of the tilt angle sequence per second within the minute preceding the current moment is obtained. The mean of the absolute values of all elements in the first-order difference sequence is used as the tilt fluctuation of the UAV fuselage per second within the minute preceding the current moment. The tilt fluctuation reflects the tilt fluctuation characteristics of the quadcopter UAV when inspecting power transmission towers at high altitudes. The greater the tilt fluctuation, the worse the stability of the UAV fuselage during the power transmission tower inspection, requiring timely attitude control of the UAV to improve the efficiency and quality of power transmission tower inspection.
[0069] Generally, when using quadcopter drones to inspect power transmission towers, if the correlation between the wind disturbance characteristics of any rotor on the drone and the tilt fluctuation characteristics of the drone fuselage is high, and the degree of wind disturbance on any rotor continuously increases, it indicates that the lift at the location of any rotor is more strongly suppressed by the wind, causing unstable tilt fluctuations in the drone fuselage.
[0070] To analyze the lift suppression effect at each rotor position at the current moment, the disturbance values of each rotor in the minute before the current moment are arranged in time sequence to form the disturbance sequence of each rotor. The tilt fluctuation of the UAV fuselage in the minute before the current moment is arranged in time sequence to form the tilt fluctuation sequence of the UAV fuselage.
[0071] The above analysis reveals the correlation between the disturbance sequence and the tilting wave sequence of each rotor.
[0072] In this embodiment, the covariance is used to measure the correlation between the perturbation sequence and the tilted fluctuation sequence. The calculation of covariance is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to measure the correlation between the perturbation sequence and the tilted fluctuation sequence, implementers may use other existing techniques, such as Pearson correlation coefficient, etc. This application does not impose any special restrictions.
[0073] Furthermore, the sum of all positive numbers in the first-order difference sequence of the disturbance sequence of each rotor is obtained. The product of the correlation degree and the sum is used as the disturbance factor of each rotor position at the current moment. The disturbance factor reflects the lift suppression effect at each rotor position at the current moment. If the incremental feature of the wind disturbance on any rotor is larger and the correlation between the disturbance sequence and the tilt fluctuation sequence is greater, it can better illustrate the influence of the wind disturbance feature of any rotor on the tilt fluctuation of the UAV fuselage. To a certain extent, it shows that the lift suppression effect at any rotor position is stronger at this time, causing the quadcopter UAV to have lift instability. At this time, it is necessary to control the attitude of the UAV in time.
[0074] During the inspection of power transmission towers using quadcopter drones, significant differences in lift at the four rotor positions due to wind suppression at high altitudes can cause considerable instability in the drone's fuselage, impacting the efficiency and quality of the inspection. To improve the efficiency and quality of tower inspections, it is necessary to control and adjust the power of the brushless motors within each rotor during the quadcopter's flight, thereby controlling the drone's flight attitude.
[0075] Furthermore, based on the distribution of the disturbance factor and the preset maximum motor power of the brushless motors in each rotor, the motor adjustment power of each brushless motor in the rotor at the current moment is obtained, expressed as:
[0076] In the formula, P ′ j Let G be the motor adjustment power of the j-th brushless motor inside the rotor at the current moment; P is the power of the j-th brushless motor inside the rotor at the current moment; jThis is the normalized result of the disturbance factor at the j-th rotor position at the current moment; P is the normalized mean of the disturbance factors for all rotor positions at the current moment; max The preset maximum motor power for the brushless motor is 450W in this embodiment. Let P be the first difference. max -P is denoted as the second difference.
[0077] In this embodiment, the Softmax function is used to normalize the disturbance factor.
[0078] It should be noted that: if the disturbance factor at any rotor position during the flight of a quadcopter drone is higher than the average level of the disturbance factors at all rotor positions, the lift at that rotor position is likely to be insufficient. In this case, the power of the brushless motor in that rotor position should be appropriately increased to ensure the stability of the lift at all four rotor positions. Conversely, if the disturbance factor at any rotor position during the flight of a quadcopter drone is lower than the average level of the disturbance factors at all rotor positions, the lift at that rotor position is likely to be relatively excessive. In this case, the power of the brushless motor in that rotor position should be appropriately decreased to quickly restore the drone to a stable state. A schematic diagram of the motor power adjustment process is shown below. Figure 2 As shown.
[0079] Furthermore, the power of the brushless motors within each rotor is adjusted. Specifically, the current power of each brushless motor and its adjusted power are transmitted to the flight controller within the flight control module. The flight controller adjusts the power of the brushless motors by calculating the error between the adjusted power and the actual power. The flight controller outputs a control signal and transmits it to each electronic speed controller. Each electronic speed controller adjusts the power of each brushless motor through current transmission, bringing the power of each brushless motor to the adjusted power. The brushless motors then power the propellers of the quadcopter, ensuring stability during the inspection of power transmission towers and improving the quality and efficiency of the inspection. The power adjustment flowchart is shown below. Figure 3 As shown.
[0080] The obstacle avoidance module 103 is used to plan a flight path to avoid obstacles.
[0081] During the flight of the quadcopter drone, the obstacle avoidance module uses laser rangefinders and GPS sensors to identify obstacles around the quadcopter drone and the distance between the drone and the obstacles. Based on the surrounding obstacles and the distance between the quadcopter drone and the obstacles, the quadcopter drone plans a safe flight path to avoid collisions with the obstacles and ensure the safe flight of the drone.
[0082] The tower monitoring module 104 is used to monitor damage information of transmission towers.
[0083] The tower detection module monitors images of power transmission towers using high-definition cameras and uses these images to detect damage information of the hardware on the power transmission towers.
[0084] The wireless communication module 105 is used to transmit the damage information to the ground station.
[0085] The wireless communication module is used to communicate with the ground station, transmitting information about damage to the hardware on the transmission tower to the ground station and notifying maintenance personnel to repair the faulty transmission tower.
[0086] In summary, this application measures the wind disturbance characteristics and tilt fluctuation characteristics of each rotor during the flight of a quadcopter drone by collecting tilt angle, wind speed, and rotational speed. It also considers both wind disturbance and tilt fluctuation characteristics to comprehensively analyze and measure the lift suppression effect at each rotor position during the drone's flight. Furthermore, it utilizes the differences in lift suppression effects between rotor positions to precisely control and adjust the brushless motor, thereby improving the accuracy of correcting the drone's flight attitude.
[0087] This application compensates for the impact of wind speed by accurately controlling and adjusting the power of the brushless motors in each rotor, ensuring the stability of the quadcopter drone when flying in harsh environments, improving the clarity of the images taken of power transmission towers, and thus improving the quality and efficiency of power transmission tower inspections.
[0088] This application identifies obstacles around the quadcopter drone and the distance between the drone and the obstacles through a flight obstacle avoidance module, plans a safe flight path in advance, avoids collisions with obstacles, and improves the safety of the quadcopter drone during flight.
[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0090] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.
Claims
1. An integrated UAV flight control and monitoring system for power transmission tower inspection, characterized in that, The system includes: The flight monitoring module is used to acquire the wind speed, rotation speed of each rotor of the drone, and tilt angle of the drone fuselage in real time. The flight control module is used to obtain the disturbance value of each rotor in each preset time period by analyzing the correlation between wind speed and the rotation speed of each rotor in each preset time period, as well as the dispersion of the rotation speed of each rotor. By analyzing the changes in the tilt angle within each preset time period, the tilt fluctuation of the drone body within each preset time period is obtained. By analyzing the correlation between the disturbance value and the tilt fluctuation, and combining the growth of the disturbance value within all the preset time periods, the disturbance factor of each rotor position at the current moment is obtained; Based on the distribution of the disturbance factors and the preset maximum motor power of the brushless motors in each rotor, the power of each brushless motor in the rotor is adjusted at the current moment. The obstacle avoidance module is used to plan flight paths to avoid obstacles; The tower monitoring module is used to monitor damage information of transmission towers; A wireless communication module is used to transmit the damage information to a ground station; The process of obtaining the disturbance value is as follows: The wind speeds within each preset time period are arranged in chronological order to form wind speed sequences. The rotational speeds of each rotor within each preset time period are arranged in chronological order to form a sequence of rotational speeds for each preset time period. Calculate the grey correlation coefficient between the wind speed sequence and the rotational speed sequence for each preset time period; The disturbance value is obtained by the grey relational coefficient and the dispersion. The perturbation value is the product of the gray correlation coefficient and the dispersion. The process of obtaining the tilt fluctuation is as follows: The tilt angles within each preset time period are arranged in chronological order to form a sequence of tilt angles; The tilt volatility is the mean of the absolute values of all elements in the first-order difference sequence of the corresponding tilt angle sequence; The process of obtaining the disturbance factor is as follows: The disturbance values of each rotor within all the preset time periods are arranged in chronological order to form the disturbance sequence of each rotor. Arrange the tilt fluctuations of the drone body within all the preset time periods in chronological order to form a tilt fluctuation sequence of the drone body; Calculate the correlation between each disturbance sequence and the tilted fluctuation sequence; Calculate the sum of all positive numbers in the first-order difference sequence of each perturbation sequence; By combining the correlation degree and the sum, the disturbance factor of each rotor position at the current moment is obtained; The disturbance factor is the product of the correlation degree and the sum.
2. The integrated UAV flight control and monitoring system for power transmission tower inspection as described in claim 1, characterized in that, The adjustment of the power of the brushless motors in each rotor at the current moment includes: Calculate the average of the normalized values of the disturbance factors for all rotor positions at the current moment; By comparing the normalized value of the disturbance factor at each rotor position at the current moment with the average value, and the difference between the maximum motor power and the power of the brushless motor in each rotor at the current moment, the motor adjustment power of the brushless motor in each rotor at the current moment is obtained. The power of the brushless motors in each rotor blade is adjusted by regulating the power of the motor.
3. The integrated UAV flight control and monitoring system for power transmission tower inspection as described in claim 2, characterized in that, The process of obtaining the motor's adjustable power is as follows: The difference between the normalized value of the disturbance factor at each rotor position at the current moment and the average value is denoted as the first difference. The difference between the maximum motor power and the power of the brushless motor in each rotor at the current moment is recorded as the second difference. By combining the power of the brushless motors in each rotor blade at the current moment with the first difference and the second difference, the motor adjustment power of the brushless motors in each rotor blade at the current moment can be obtained.
4. The integrated UAV flight control and monitoring system for power transmission tower inspection as described in claim 3, characterized in that, The calculation process for the motor's adjustable power is as follows: Calculate the product of the first difference and the second difference, whereby the motor adjustment power is the sum of the power of the brushless motors in each rotating blade at the current moment and the product.
5. The integrated UAV flight control and monitoring system for power transmission tower inspection as described in claim 2, characterized in that, The adjustment of the power of the brushless motors in each rotor blade at the current moment includes: The power of the brushless motors in each rotor at the current moment, along with the motor adjustment power, is input into the flight controller in the flight control module. The flight controller outputs control signals and transmits them to each electronic regulator. Each electronic regulator adjusts the power of each brushless motor through current transmission.