Six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors

Abstract

The invention provides a six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors, and the method comprises the steps: carrying a scalar magnetic sensor and a vector magnetic sensor on a six-rotor unmanned aerial vehicle, and collecting the real-time flight parameters of the six-rotor unmanned aerial vehicle through a flight parameter collection device; measuring the magnetic field intensity of the position where the unmanned aerial vehicle is located through a scalar magnetic sensor, and measuring the magnetic field vector of the position where the unmanned aerial vehicle is located through a vector magnetic sensor; inputting the real-time flight parameters, the magnetic field intensity and the magnetic field vector of the unmanned aerial vehicle into a parameter compensation device; the parameter compensation device adopts a comprehensive error compensation model to perform real-time compensation on the magnetic field intensity according to the real-time flight parameters, the magnetic field intensity and the magnetic field vector of the unmanned aerial vehicle; the flight controller adjusts the flight state of the unmanned aerial vehicle according to the compensation result. According to the technical scheme, the technical problems that in the prior art, an aeromagnetic compensation method is not high in compensation precision and low in response speed are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) aeromagnetic measurement, and in particular to an aeromagnetic compensation method for a six-rotor UAV based on scalar and vector magnetic sensors. Background Art

[0002] In UAV aeromagnetic measurement, errors occur in the measurement data due to the magnetic field interference of the UAV itself. In order to improve the measurement accuracy, these errors need to be compensated. Existing compensation methods mostly rely on scalar sensors or vector sensors, but these methods have certain limitations in practical applications, such as low compensation accuracy and slow response speed. Especially for hexacopter UAVs, their structural complexity and symmetry put forward higher requirements on the layout of sensors and compensation algorithms. Summary of the invention

[0003] The present invention provides an aeromagnetic compensation method for a six-rotor unmanned aerial vehicle based on scalar and vector magnetic sensors, which can solve the technical problems of low compensation accuracy and slow response speed of the aeromagnetic compensation method in the prior art.

[0004] According to one aspect of the present invention, a method for aeromagnetic compensation of a six-rotor UAV based on scalar and vector magnetic sensors is provided. The method for aeromagnetic compensation of a six-rotor UAV based on scalar and vector magnetic sensors comprises: step one, carrying a scalar magnetic sensor and a vector magnetic sensor on the six-rotor UAV, and collecting real-time flight parameters of the six-rotor UAV through a flight parameter acquisition device; step two, measuring the magnetic field strength at the location of the UAV through the scalar magnetic sensor, and measuring the magnetic field vector at the location of the UAV through the vector magnetic sensor; step three, inputting the real-time flight parameters, magnetic field strength and magnetic field vector of the UAV into a parameter compensation device; step four, the parameter compensation device uses a comprehensive error compensation model to perform real-time compensation for the magnetic field strength according to the real-time flight parameters, magnetic field strength and magnetic field vector of the UAV; step five, the flight controller adjusts the flight state of the UAV according to the compensation result, and completes the aeromagnetic compensation of the six-rotor UAV based on scalar and vector magnetic sensors.

[0005] Furthermore, the comprehensive error compensation model of the parameter compensation device is: in, It is a transformation matrix composed of the attitude angles of the drone, which is used to convert the measured values ​​from the body coordinate system to the geographic coordinate system. is the estimated value of the drone’s own magnetic field interference, is the measured value vector of the drone magnetic field vector sensor, is the attitude angle vector of the drone, φ is the pitch angle, θ is the roll angle, ψ is the yaw angle, is the compensated magnetic field.

[0006] Furthermore, the comprehensive error compensation model is used to compensate the magnetic field strength in real time, specifically including: calculating the transformation matrix from the body coordinate system to the geographic coordinate system Using the data from the scalar magnetic sensor and the vector magnetic sensor, combined with the flight parameters of the drone, the machine learning algorithm is used to estimate the drone's own magnetic field interference. Estimate of; transform the matrix Estimation of the drone's own magnetic field disturbance And the drone magnetic field vector sensor measurement value vector Substitute into the comprehensive error compensation model and calculate the compensated magnetic field

[0007] Furthermore, support vector machines (SVM) or neural networks are used to learn the pattern of the drone’s own magnetic field interference and predict it in real time.

[0008] Furthermore, the transformation matrix from the body coordinate system to the geographic coordinate system is for

[0009]

[0010] According to another aspect of the present invention, a hexacopter aeromagnetic compensation system based on scalar and vector magnetic sensors is provided. The hexacopter aeromagnetic compensation system based on scalar and vector magnetic sensors uses the hexacopter aeromagnetic compensation method based on scalar and vector magnetic sensors as described above to perform hexacopter aeromagnetic compensation.

[0011] Furthermore, the aeromagnetic compensation system of the six-rotor UAV based on scalar and vector magnetic sensors includes a flight parameter acquisition device, a scalar magnetic sensor, a vector magnetic sensor, a parameter compensation device and a flight controller. The flight parameter acquisition device is used to collect real-time flight parameters of the UAV, the scalar magnetic sensor is used to measure the magnetic field strength at the location of the UAV, the vector magnetic sensor is used to measure the magnetic field vector at the location of the UAV, the parameter compensation device is used to compensate for the magnetic field strength in real time according to the real-time flight parameters and the magnetic field measurement data, and the flight controller is used to control the flight of the UAV and the operation of the parameter compensation device.

[0012] Furthermore, the real-time flight parameters of the UAV include flight speed, flight altitude and flight attitude.

[0013] Further, the scalar magnetic sensor includes a three-axis magnetometer, and the vector magnetic sensor includes a three-axis fluxgate sensor.

[0014] Furthermore, scalar magnetic sensors are evenly distributed near the six rotors of the UAV to reduce the influence of the UAV’s own magnetic field on the measurement results, and vector magnetic sensors are installed at the center of the UAV to obtain more accurate magnetic field vector data.

[0015] By applying the technical solution of the present invention, a six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors is provided. The compensation method can combine the advantages of scalar and vector magnetic sensors, input the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle into the parameter compensation device, and the parameter compensation device uses a comprehensive error compensation model to compensate the magnetic field strength in real time according to the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle, thereby improving the accuracy and real-time performance of the compensation, and is suitable for the aeromagnetic measurement of the six-rotor unmanned aerial vehicle in a complex magnetic field environment. By estimating the magnetic field interference of the unmanned aerial vehicle itself through a machine learning algorithm, the accuracy and adaptability of the compensation can be further improved. Therefore, compared with the prior art, the six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors provided by the present invention can effectively improve the accuracy and real-time performance of aeromagnetic measurement, and has a wide range of application prospects. By combining the distribution mode of sensors and machine learning algorithms, the present invention can adapt to different flight conditions and environments and provide more accurate aeromagnetic measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The included drawings are used to provide a further understanding of the embodiments of the present invention, which constitute a part of the specification, are used to illustrate the embodiments of the present invention, and together with the text description, explain the principles of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of an aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors provided according to a specific embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments in 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.

[0019] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0020] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be regarded as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0021] like Figure 1 As shown, according to a specific embodiment of the present invention, a six-rotor UAV aeromagnetic compensation method based on scalar and vector magnetic sensors is provided, and the six-rotor UAV aeromagnetic compensation method based on scalar and vector magnetic sensors includes: step one, carrying a scalar magnetic sensor and a vector magnetic sensor on the six-rotor UAV, and collecting the real-time flight parameters of the six-rotor UAV through a flight parameter acquisition device; step two, measuring the magnetic field strength at the location of the UAV through the scalar magnetic sensor, and measuring the magnetic field vector at the location of the UAV through the vector magnetic sensor; step three, inputting the real-time flight parameters, magnetic field strength and magnetic field vector of the UAV into the parameter compensation device; step four, the parameter compensation device uses a comprehensive error compensation model to perform real-time compensation for the magnetic field strength according to the real-time flight parameters, magnetic field strength and magnetic field vector of the UAV; step five, the flight controller adjusts the flight state of the UAV according to the compensation result, and completes the six-rotor UAV aeromagnetic compensation based on scalar and vector magnetic sensors.

[0022] By applying this configuration, a six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors is provided. The compensation method can combine the advantages of scalar and vector magnetic sensors, input the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle into the parameter compensation device, and the parameter compensation device uses a comprehensive error compensation model to compensate the magnetic field strength in real time according to the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle, thereby improving the accuracy and real-time performance of the compensation, and is suitable for the aeromagnetic measurement of the six-rotor unmanned aerial vehicle in a complex magnetic field environment. By estimating the magnetic field interference of the unmanned aerial vehicle itself through a machine learning algorithm, the accuracy and adaptability of the compensation can be further improved. Therefore, compared with the prior art, the six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors provided by the present invention can effectively improve the accuracy and real-time performance of aeromagnetic measurement, and has a wide range of application prospects. By combining the distribution mode of sensors and machine learning algorithms, the present invention can adapt to different flight conditions and environments and provide more accurate aeromagnetic measurement data.

[0023] Furthermore, in the present invention, the comprehensive error compensation model of the parameter compensation device is: in, It is a transformation matrix composed of the attitude angles of the drone, which is used to convert the measured values ​​from the body coordinate system to the geographic coordinate system. is the estimated value of the drone’s own magnetic field interference, is the measured value vector of the drone magnetic field vector sensor, is the attitude angle vector of the drone, φ is the pitch angle, θ is the roll angle, ψ is the yaw angle, is the compensated magnetic field.

[0024] In the present invention, the comprehensive error compensation model is used to compensate the magnetic field strength in real time, specifically including: calculating the transformation matrix from the body coordinate system to the geographic coordinate system Using the data from the scalar magnetic sensor and the vector magnetic sensor, combined with the flight parameters of the drone, the machine learning algorithm is used to estimate the drone's own magnetic field interference. Estimate of; transform the matrix Estimation of the drone's own magnetic field disturbance And the drone magnetic field vector sensor measurement value vector Substitute into the comprehensive error compensation model and calculate the compensated magnetic field

[0025] As a specific embodiment of the present invention, a support vector machine (SVM) or a neural network is used to learn the pattern of the magnetic field interference of the drone itself and predict in real time Transformation matrix from body coordinate system to geographic coordinate system for

[0026] According to another aspect of the present invention, a hexacopter aeromagnetic compensation system based on scalar and vector magnetic sensors is provided. The hexacopter aeromagnetic compensation system based on scalar and vector magnetic sensors uses the hexacopter aeromagnetic compensation method based on scalar and vector magnetic sensors as described above to perform hexacopter aeromagnetic compensation.

[0027] By applying this configuration, a six-rotor unmanned aerial vehicle aeromagnetic compensation system based on scalar and vector magnetic sensors is provided. The compensation system can combine the advantages of scalar and vector magnetic sensors, input the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle into the parameter compensation device, and the parameter compensation device uses a comprehensive error compensation model to compensate the magnetic field strength in real time according to the real-time flight parameters, magnetic field strength and magnetic field vector of the unmanned aerial vehicle, thereby improving the accuracy and real-time performance of the compensation, and is suitable for the aeromagnetic measurement of the six-rotor unmanned aerial vehicle in a complex magnetic field environment. By estimating the magnetic field interference of the unmanned aerial vehicle itself through a machine learning algorithm, the accuracy and adaptability of the compensation can be further improved. Therefore, compared with the prior art, the six-rotor unmanned aerial vehicle aeromagnetic compensation system based on scalar and vector magnetic sensors provided by the present invention can effectively improve the accuracy and real-time performance of aeromagnetic measurement, and has a wide range of application prospects. By combining the distribution mode of sensors and machine learning algorithms, the present invention can adapt to different flight conditions and environments and provide more accurate aeromagnetic measurement data.

[0028] Furthermore, in the present invention, a six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors includes a flight parameter acquisition device, a scalar magnetic sensor, a vector magnetic sensor, a parameter compensation device and a flight controller. The flight parameter acquisition device is used to collect real-time flight parameters of the UAV, the scalar magnetic sensor is used to measure the magnetic field strength at the location of the UAV, the vector magnetic sensor is used to measure the magnetic field vector at the location of the UAV, the parameter compensation device is used to compensate for the magnetic field strength in real time according to the real-time flight parameters and magnetic field measurement data, and the flight controller is used to control the flight of the UAV and the operation of the parameter compensation device.

[0029] As a specific embodiment of the present invention, the real-time flight parameters of the UAV include flight speed, flight altitude and flight attitude. The scalar magnetic sensor includes a three-axis magnetometer, and the vector magnetic sensor includes a three-axis fluxgate sensor.

[0030] Furthermore, in the present invention, in order to further reduce the influence of the drone's own magnetic field on the measurement results and improve the measurement accuracy, scalar magnetic sensors are evenly distributed near the six rotors of the drone to reduce the influence of the drone's own magnetic field on the measurement results, and vector magnetic sensors are installed at the center of the drone to obtain more accurate magnetic field vector data.

[0031] In order to further understand the present invention, the following Figure 1 The aeromagnetic compensation method and system for a six-rotor unmanned aerial vehicle based on scalar and vector magnetic sensors provided by the present invention are described in detail.

[0032] like Figure 1 As shown, according to a specific embodiment of the present invention, a six-rotor UAV aeromagnetic compensation method based on scalar and vector magnetic sensors is provided to improve the accuracy and real-time performance of aeromagnetic measurement, and is particularly optimized for the characteristics of the six-rotor UAV and the distribution of sensors.

[0033] To achieve the above object, the present invention provides the following solutions:

[0034] 1. System composition

[0035] An aeromagnetic compensation system for a six-rotor unmanned aerial vehicle comprises a flight parameter acquisition device, a scalar magnetic sensor, a vector magnetic sensor, a parameter compensation device, a flight controller and the like.

[0036] Flight parameter collection device: used to collect real-time flight parameters of the drone, including but not limited to flight speed, flight altitude, flight attitude (pitch angle, roll angle, yaw angle);

[0037] Scalar magnetic sensor: used to measure the magnetic field strength at the drone’s location, which can be a three-axis magnetometer;

[0038] Vector magnetic sensor: used to measure the magnetic field vector of the drone's location, which can be a three-axis fluxgate sensor;

[0039] Parameter compensation device: used to compensate the magnetic field strength in real time according to the real-time flight parameters and magnetic field measurement data, which can be an embedded computing module, such as an ARM processor;

[0040] Flight controller: used to control the flight of the drone and the work of the compensation device.

[0041] 2. Magnetic sensor distribution

[0042] The distribution of magnetic sensors for magnetic field data collection of six-rotor drones is as follows:

[0043] Scalar magnetic sensors: Evenly distributed near the six rotors of the drone to reduce the impact of the drone’s own magnetic field on the measurement results. The specific distance depends on the actual situation of the drone.

[0044] Vector magnetic sensor: Installed at the center of the drone to obtain more accurate magnetic field vector data.

[0045] 3. Compensation method

[0046] A six-rotor unmanned aerial vehicle aeromagnetic compensation method based on scalar and vector magnetic sensors comprises the following steps:

[0047] Step 1: Collect the real-time flight parameters of the UAV through the flight parameter collection device;

[0048] Step 2: Use the scalar magnetic sensor and the vector magnetic sensor to measure the magnetic field strength and magnetic field vector of the drone’s location respectively;

[0049] Step 3: Input the flight parameters and magnetic field measurement data into the parameter compensation device;

[0050] Step 4: The parameter compensation device uses an algorithm model to compensate the magnetic field strength in real time according to the flight parameters and magnetic field measurement data;

[0051] Step 5: The flight controller adjusts the flight state of the drone according to the compensation results to achieve more accurate aeromagnetic measurement.

[0052] 4. Algorithm Model

[0053] The algorithm model in the parameter compensation device can adopt the following methods:

[0054] 4.1 Comprehensive error compensation model

[0055] Assume that the measured value vector of the drone magnetic field vector sensor is The attitude angle vector of the drone is Where φ is the pitch angle, θ is the roll angle, and ψ is the yaw angle. The magnetic field compensation model of the drone can be expressed as:

[0056]

[0057] in, It is a transformation matrix composed of the attitude angles of the drone, which is used to convert the measurements from the body coordinate system to the geographic coordinate system. is an estimate of the drone’s own magnetic field disturbance.

[0058] 4.2 Derivation of compensation formula

[0059] Based on the attitude angle of the drone, we can derive the magnetic field compensation formula. First, we need to calculate the transformation matrix from the body coordinate system to the geographic coordinate system. This is then applied to the magnetic field measurements The compensated magnetic field

[0060]

[0061] in, The estimation of can be done by combining the data of scalar magnetic sensors and vector magnetic sensors with the flight parameters of the drone through machine learning algorithms. For example, a support vector machine (SVM) or a neural network can be used to learn the pattern of the drone's own magnetic field interference and predict it in real time.

[0062] The compensation method of the present invention can combine the advantages of scalar and vector magnetic sensors to improve the accuracy and real-time performance of compensation, and is suitable for aeromagnetic measurement of six-rotor drones in complex magnetic field environments. By optimizing the distribution of sensors, the influence of the drone's own magnetic field on the measurement results can be further reduced, and the accuracy of the measurement can be improved. In addition, by estimating the drone's own magnetic field interference through machine learning algorithms, the accuracy and adaptability of compensation can be further improved.

[0063] The present invention provides an aeromagnetic compensation method for a six-rotor UAV based on scalar and vector magnetic sensors, which can effectively improve the accuracy and real-time performance of aeromagnetic measurement and has broad application prospects. By combining the distribution mode of sensors and machine learning algorithms, the present invention can adapt to different flight conditions and environments and provide more accurate aeromagnetic measurement data.

[0064] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0065] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A six-rotor UAV aeromagnetic compensation method based on scalar and vector magnetic sensors, characterized in that: The aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors includes: Step 1: A scalar magnetic sensor and a vector magnetic sensor are mounted on the six-rotor drone, and the real-time flight parameters of the six-rotor drone are collected through a flight parameter collection device; Step 2: measuring the magnetic field strength at the location of the drone by using the scalar magnetic sensor, and measuring the magnetic field vector at the location of the drone by using the vector magnetic sensor; Step 3, inputting the real-time flight parameters of the UAV, the magnetic field strength and the magnetic field vector into a parameter compensation device; Step 4, the parameter compensation device uses a comprehensive error compensation model to perform real-time compensation for the magnetic field intensity according to the real-time flight parameters of the UAV, the magnetic field intensity and the magnetic field vector; Step 5: The flight controller adjusts the flight state of the UAV according to the compensation results, completing the aeromagnetic compensation of the six-rotor UAV based on scalar and vector magnetic sensors.

2. The aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors according to claim 1, characterized in that: The comprehensive error compensation model of the parameter compensation device is: in, It is a transformation matrix composed of the attitude angles of the drone, which is used to convert the measured values ​​from the body coordinate system to the geographic coordinate system. is the estimated value of the drone’s own magnetic field interference, is the measured value vector of the drone magnetic field vector sensor, is the attitude angle vector of the drone, φ is the pitch angle, θ is the roll angle, ψ is the yaw angle, is the compensated magnetic field.

3. The aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors according to claim 2, characterized in that: The comprehensive error compensation model is used to compensate the magnetic field strength in real time, including: Compute the transformation matrix from body coordinate system to geographic coordinate system Using the data from the scalar magnetic sensor and the vector magnetic sensor, combined with the flight parameters of the drone, the machine learning algorithm is used to estimate the drone's own magnetic field interference. estimates; Transform the matrix Estimation of the drone's own magnetic field disturbance And the drone magnetic field vector sensor measurement value vector Substitute into the comprehensive error compensation model and calculate the compensated magnetic field 4. The aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors according to claim 3 is characterized in that: Use support vector machines (SVM) or neural networks to learn the pattern of the drone’s own magnetic field interference and predict it in real time 5. The aeromagnetic compensation method for a six-rotor drone based on scalar and vector magnetic sensors according to claim 4, characterized in that: The transformation matrix from the body coordinate system to the geographic coordinate system for 6. A six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors, characterized in that: The hexacopter aeromagnetic compensation system based on scalar and vector magnetic sensors uses the hexacopter aeromagnetic compensation method based on scalar and vector magnetic sensors as described in claims 1 to 5 to perform hexacopter aeromagnetic compensation.

7. The six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors according to claim 6, characterized in that: The six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors includes a flight parameter acquisition device, a scalar magnetic sensor, a vector magnetic sensor, a parameter compensation device and a flight controller. The flight parameter acquisition device is used to acquire real-time flight parameters of the UAV, the scalar magnetic sensor is used to measure the magnetic field strength at the location of the UAV, the vector magnetic sensor is used to measure the magnetic field vector at the location of the UAV, the parameter compensation device is used to compensate for the magnetic field strength in real time according to the real-time flight parameters and magnetic field measurement data, and the flight controller is used to control the flight of the UAV and the operation of the parameter compensation device.

8. The six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors according to claim 7, characterized in that: The real-time flight parameters of the UAV include flight speed, flight altitude and flight attitude.

9. The six-rotor UAV aeromagnetic compensation system based on scalar and vector magnetic sensors according to claim 8, characterized in that: The scalar magnetic sensor includes a three-axis magnetometer, and the vector magnetic sensor includes a three-axis fluxgate sensor.

10. The six-rotor unmanned aerial vehicle aeromagnetic compensation system based on scalar and vector magnetic sensors according to claim 9, characterized in that: The scalar magnetic sensors are evenly distributed near the six rotors of the drone to reduce the influence of the drone's own magnetic field on the measurement results, and the vector magnetic sensor is installed at the center of the drone to obtain more accurate magnetic field vector data.

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