Unmanned aerial vehicle aeromagnetic array measurement system and method

Abstract

The invention discloses an unmanned aerial vehicle aeromagnetic array measurement system and method, and the method comprises the steps: scanning the three-dimensional terrain of a preset exploration region, constructing a landform model, extracting the terrain features, calculating the terrain complexity, carrying out the region clustering, obtaining an exploration region set with different complexities, and carrying out the measurement of the three-dimensional terrain in each exploration region, the method comprises the steps of determining a control period by combining meteorological characteristic gradient change, constructing a motion model according to attitude and position dynamics of an unmanned aerial vehicle, solving energy efficiency optimization and flight stability problems, determining an optimal control sequence, obtaining a motion trail, correcting the motion trail based on electromagnetic interference, and determining a magnetometer array structure according to precision requirements. And performing multi-unmanned aerial vehicle formation magnetic field measurement, and adjusting the multi-unmanned aerial vehicle formation according to the magnetic field difference of the overlapping region. According to the method, multiple factors can be comprehensively considered, the motion trail and the formation mode of the unmanned aerial vehicle are optimized, the exploration efficiency and the measurement precision are improved, the method is suitable for exploration tasks in a complex environment, and meanwhile good interpretability is achieved.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration, and in particular to an unmanned aerial vehicle (UAV) aeromagnetic array measurement system and method. Background Art

[0002] In fields such as geological exploration, traditional ground magnetic measurement methods are greatly restricted by terrain, with low efficiency and difficulty in covering large areas. There are problems such as low accuracy and complex data processing, and they cannot meet the requirements of high-precision exploration. At the same time, for measurements in complex terrain environments, traditional systems lack effective coping strategies, resulting in large errors in measurement results and affecting the accuracy of subsequent geological analysis and resource assessment.

[0003] Applying UAVs to aeromagnetic array measurement can effectively overcome some defects of traditional measurement methods. However, existing UAV aeromagnetic measurement technologies still need to be improved in terms of integration, stability, and terrain adaptability. Among them, UAVs are easily interfered by multiple factors during flight, affecting the accuracy of aeromagnetic data, and lack intelligent measurement methods for different terrains and landforms, and cannot fully utilize the advantages of UAVs.

[0004] An unmanned aerial vehicle aeromagnetic array measurement system and method, by integrating intelligent control algorithms and efficient data processing methods, constructs a more efficient, accurate, and terrain-adaptive measurement system and method to achieve rapid and accurate exploration of underground resources. Summary of the Invention

[0005] The object of the present invention is to provide an unmanned aerial vehicle aeromagnetic array measurement system and method.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions: The first aspect of the present invention provides an unmanned aerial vehicle aeromagnetic array measurement method, including: Scanning the three-dimensional terrain of a preset exploration area to construct a terrain and landform model; Extracting the terrain features of the terrain and landform model, calculating the terrain complexity of the preset exploration area, and performing regional clustering based on the terrain complexity to obtain a set of exploration areas with different terrain complexities; In each exploration area, determining the control period by combining the gradient change of the meteorological characteristics of the area; Constructing a motion model of the UAV according to the attitude dynamics and position dynamics of the UAV, and in each control period, determining the optimal control sequence of the motion model of the UAV by solving the problems of energy efficiency optimization and flight stability to obtain the motion trajectory of the UAV; Based on the electromagnetic interference received by the UAV, correcting and compensating the motion trajectory of the UAV; Determine the structure of the magnetometer array of the unmanned aerial vehicle according to the accuracy requirements of the preset exploration area, and conduct multi-unmanned aerial vehicle formation based on the movement trajectory of the unmanned aerial vehicle to perform magnetic field measurement; Adjust the multi-unmanned aerial vehicle formation based on the magnetic field differences of different unmanned aerial vehicles in the overlapping area.

[0007] As a further method, the method for extracting the terrain features of the terrain and landform model and calculating the terrain complexity of the preset exploration area includes: Perform grid division on the terrain and landform model. For the elevation data in the terrain and landform model, use the difference method to calculate the terrain undulation, slope, and aspect change rate; Perform second-order difference operation on the elevation data to obtain the curvature of the terrain at different positions. The expression is:

[0008] Among them, is the terrain curvature value at the position, is the elevation value in the terrain and landform model at the position, and are respectively in the direction and direction of the second-order partial derivatives, is the mixed second-order partial derivative of , and are respectively in the direction and direction of the first-order partial derivatives, that is, the elevation change rate; Calculate the terrain complexity. The expression is:

[0009] Among them, is the terrain complexity at the position, and are respectively the number of grids of the terrain and landform model in the row and column directions, , , and are respectively the weight coefficients of the terrain undulation, slope, aspect change rate, and terrain curvature, is the terrain undulation at the position, is the average value of the terrain undulation, is the slope at the position, is the maximum slope in all positions, is the slope direction change rate at position, is the terrain curvature at position, is the maximum terrain curvature in all positions.

[0010] As a further method, the method for performing regional clustering based on terrain complexity to obtain a set of exploration areas with different terrain complexities includes: Identifying points with a sharp change in the terrain complexity gradient as clustering centers, specifically: for each grid node center, selecting nodes with a modulus length of terrain complexity greater than a preset threshold as clustering centers; Calculating the similarity of terrain complexity between each grid and the clustering center, and the expression is:

[0011] where, represents the similarity of terrain complexity between the grid at position and the clustering center , is the sensitivity coefficient of the similarity function, and are the terrain complexities at position and the clustering center respectively, is the attenuation amplitude of the similarity function when the difference changes; Normalizing the similarity from 0 to 1, and clustering grids with a similarity greater than 0.7 with the clustering center into one category to obtain a set of exploration areas with different terrain complexities.

[0012] As a further method, the method for determining the control period by combining the gradient change of the meteorological characteristics of the area includes: Real-time collecting meteorological factors of each exploration area, including wind speed, wind direction, temperature and humidity; Constructing a multi-layer perceptron model to fit the flight situation of the UAV under different meteorological conditions, training the model with historical flight data, and determining the feature importance of each meteorological factor for normal flight; Obtaining the gradient change rate of meteorological characteristics, and calculating the control period according to the sensitivity coefficients of each meteorological factor, and the expression is:

[0013] where, is the preset control period of the UAV under ideal meteorological conditions, , , and They are the weight coefficients of wind speed, wind direction, temperature, and humidity respectively. , , and They are the collected values of wind speed, wind direction, temperature, and humidity at the position respectively. , , and They are the characteristic importances of wind speed, wind direction, temperature, and humidity respectively.

[0014] As a further method, the method of constructing the motion model of the unmanned aerial vehicle according to the attitude dynamics and position dynamics of the unmanned aerial vehicle includes: Establish the attitude dynamics equation describing the attitude change of the unmanned aerial vehicle and the position dynamics equation describing the position change of the unmanned aerial vehicle through an adaptive control algorithm; the attitude dynamics equation includes the inertial parameters, aerodynamic parameters, and control inputs of the unmanned aerial vehicle; the position dynamics equation includes the initial position, velocity, and acceleration of the unmanned aerial vehicle. Take the inertial parameters, aerodynamic parameters, initial position, velocity, and acceleration of the unmanned aerial vehicle as state variables and construct a state vector. Establish the state equation of the unmanned aerial vehicle according to the attitude dynamics equation and the position dynamics model, use the control input as the input of the state equation, describe the evolution process of the state vector over time, and obtain the motion model of the unmanned aerial vehicle.

[0015] As a further method, the method of determining the optimal control sequence of the motion model of the unmanned aerial vehicle by solving the problems of energy efficiency optimization and flight stability and obtaining the flight trajectory of the unmanned aerial vehicle includes: The objective function of energy efficiency optimization, the expression is:

[0016] Where, is the control input of the unmanned aerial vehicle at time , is the number of sampling times within the control period, and are the th and th sampling times, and are the power consumption value and the task completion progress at time respectively, is the variance of the energy efficiency change, is the power consumption value at time ; The objective function of flight stability, the expression is:

[0017] Among them, is the flight time of the UAV, is the position vector of the UAV in space, is the weight coefficient of the change rate of acceleration in the optimization objective, represents the acceleration vector, represents the change rate vector of acceleration; A multi-objective optimization problem is constructed to simultaneously minimize two objective functions, and a multi-objective optimization algorithm is used to solve the optimal control sequence through iterative calculation; The obtained optimal control sequence is applied to the motion model of the UAV to obtain the flight trajectory.

[0018] As a further method, the method for correcting and compensating the motion trajectory of the UAV based on the electromagnetic interference received by the UAV includes: The UAV sensor collects the electromagnetic interference intensity, frequency and phase in real time, and locates the electromagnetic interference source in combination with the position of the UAV; Perform feature analysis on the spectrum and time domain characteristics of the electromagnetic interference source. According to the feature analysis results, construct an interference force function for the influence of electromagnetic interference on the flight of the UAV, and the expression is:

[0019] Among them, is the number of periodic interference sources, is the th periodic interference source at time spectrum feature vector, is the number of non-periodic interference sources, is the th non-periodic interference source at time time domain feature vector; Establish a model for correcting the flight trajectory of the UAV in combination with the influence function, and the expression is:

[0020] Among them, is the mass of the UAV, is the acceleration vector of the UAV at time , is the gravitational acceleration vector, is the aerodynamic force vector of the UAV at time , is the thrust vector of the UAV at time ; During the flight of the drone, based on the real-time collected electromagnetic interference data and the constructed correction model, calculate the correction amount required for the drone's flight trajectory at the current moment, and correct the drone's motion trajectory.

[0021] As a further method, the method for determining the structure of the drone's magnetometer array according to the accuracy requirements of the preset exploration area and performing multi-drone formation based on the motion trajectory of the drone includes: Determine the number of drones that meet the coverage of the preset exploration area, and obtain the motion trajectory of each drone; According to the measurement accuracy requirements of the preset exploration area, determine the number and type of magnetometers carried on each drone, as well as the layout method of the magnetometers; Ensure the preset relative position and flight synchronization between drones, perform multi-drone formation design, and use a distributed communication protocol between drones. Each drone exchanges motion states and geomagnetic gradient information with adjacent drones in real time to achieve collaborative measurement.

[0022] As a further method, the method for adjusting the multi-drone formation based on the magnetic field differences of different drones in the overlapping area includes: Collect the magnetic field data measured by each drone in the overlapping area of the preset exploration area; Calculate the magnetic field intensity difference and the magnetic field direction deviation angle, judge the positions where the magnetic field differences in the overlapping area appear to gather, diverge, and mutate, and obtain the geomagnetic gradient anomaly area; For the geomagnetic gradient anomaly area, when the geomagnetic gradient exceeds the gradient threshold, the formation density of the drones increases in proportion, increasing the measurement point density to capture the magnetic field changes.

[0023] The second aspect of the present invention provides a drone airborne magnetic array measurement system, including: A terrain modeling module for scanning the three-dimensional terrain of the preset exploration area and constructing a terrain and landform model; A terrain feature clustering module for extracting the terrain features of the terrain and landform model, calculating the terrain complexity of the preset exploration area, and performing regional clustering based on the terrain complexity to obtain a set of exploration areas with different terrain complexities; A control period module for determining the control period in each exploration area in combination with the gradient change of the meteorological characteristics of the area; A trajectory control module for constructing a motion model of the drone according to the attitude dynamics and position dynamics of the drone, and determining the optimal control sequence of the motion model of the drone by solving the problems of energy efficiency optimization and flight stability in each control period to obtain the motion trajectory of the drone; An electromagnetic interference correction module, configured to correct and compensate the motion trajectory of the drone based on the electromagnetic interference received by the drone; A multi-drone formation module, configured to determine the structure of the magnetometer array of the drones according to the accuracy requirements of the preset exploration area, and perform multi-drone formation based on the motion trajectory of the drones to perform magnetic field measurement; A formation difference adjustment module, configured to adjust the multi-drone formation based on the magnetic field differences of different drones in the overlapping area.

[0024] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) Through high-precision three-dimensional terrain scanning and terrain and landform model construction, the present invention can accurately extract terrain features and calculate terrain complexity, so as to effectively cluster the preset exploration area, ensuring the pertinence and efficiency of the exploration work; (2) By constructing the attitude dynamics and position dynamics models of the drones and solving the energy efficiency optimization and flight stability problems, the present invention can determine the optimal control sequence of the drone motion model, so as to obtain an efficient drone motion trajectory, improving the measurement accuracy and efficiency; (3) The present invention takes into account the possible electromagnetic interference during the flight of the drones and provides a trajectory correction and compensation method based on interference. By real-time collecting the electromagnetic interference intensity, frequency and phase, positioning the electromagnetic interference source in combination with the drone position, and constructing a function of the influence of electromagnetic interference on the drone flight, the measurement data accuracy is further improved; (4) According to the accuracy requirements of the preset exploration area, the present invention determines the structure of the magnetometer array of the drones, and performs multi-drone formation based on the motion trajectory of the drones to perform magnetic field measurement, which not only improves the measurement coverage rate, but also improves the quality and reliability of data collection through the collaborative work of multiple drones. Description of the Drawings

[0025] Figure 1 It is a step flow chart of a drone airborne magnetic array measurement method in an embodiment of the present invention.

[0026] Figure 2 It is a logical schematic diagram of a drone airborne magnetic array measurement system in an embodiment of the present invention. Detailed Embodiments

[0027] 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Reference Figure 1 As shown, the present invention provides a UAV aeromagnetic array measurement method, comprising: S101 scans the three-dimensional terrain of the preset exploration area and constructs a terrain model; In the actual evaluation, in a case of geomagnetic exploration around a mining belt, the area is 75 square kilometers, and the terrain is complex and diverse. In order to obtain accurate terrain data, a professional long-flight fixed-wing drone with a high-precision lidar system was used to construct a 3.8-degree resolution geomagnetic survey. The 4.2-meter terrain model is clearly visible from the visual model interface. The highest peak in the area reaches 2,673.5 meters above sea level, the difference in height between the bottom of the deepest canyon and the top of the mountain is close to 1,100 meters, and the slopes in some areas are steep, with the maximum slope measured reaching 58.7°.

[0029] S102 extracts the terrain features of the terrain model, calculates the terrain complexity of the preset exploration area, performs regional clustering based on the terrain complexity, and obtains a set of exploration areas with different terrain complexities; It needs to be explained that the extraction of terrain features and calculation of complexity is due to the different terrains affecting UAV flight in different ways. Complex mountains may hinder signals and increase energy consumption, while flat areas are relatively stable. The regional collection reasonably arranges the magnetometer array structure and UAV formation. For complex and highly complex areas that may contain rich resources, high-precision, densely laid out magnetometers are configured, and multiple UAVs are closely formed to achieve fine capture of magnetic field changes; while for low-complexity areas, resource allocation is optimized to avoid excessive investment, ultimately achieving comprehensive and accurate measurement results. In the actual assessment, based on the constructed fine terrain model, the The grid is divided into grids of 20.2 meters. For the grid node located at (3, 5), through rigorous calculation using the differential method, its terrain undulation is as high as 382.4 meters, the slope reaches 46.3°, the slope direction change rate is about 0.71 radians / meter, the terrain curvature value at this node is obtained as 0.039 through second-order difference operation, and the terrain complexity of this grid node is calculated as 0.59. The terrain complexity threshold is set at 0.55, and the cluster centers are set. A total of 5 cluster centers are obtained. After normalization, the grids with a similarity exceeding 0.7 to the cluster centers are classified into one category. Finally, the 75-square-kilometer area is divided into three categories: high, medium, and low. The high-complexity area is about 22.5 square kilometers, including steep peaks, deep valleys, and complex karst areas; the medium-complexity area is about 30 square kilometers, with many hills and gentle slopes; the low-complexity area is about 22.5 square kilometers, which is an intermontane basin.

[0030] S103 In each exploration area, determine the control period in combination with the gradient change of the meteorological characteristics of the area; It should be explained that the gradient change of meteorological factors has a great impact on the flight stability, energy consumption, and measurement accuracy of the UAV. By determining the control period in real-time in combination with the meteorological characteristics of each exploration area, the UAV can quickly adjust its attitude, avoid deviating from the predetermined flight path, and ensure the accuracy of the measurement points; a reasonable period setting can also optimize the flight energy consumption, extend the endurance, and ensure the continuous progress of long-time and high-precision magnetic field measurement operations; In actual evaluation, in each exploration area, the climate characteristics are collected. For the exploration area with high complexity, the wind speed reaches 18 m / s, the temperature drops suddenly from 28°C to 20°C, and the humidity rises from 65% to 88%. A multi-layer perceptron model is constructed and trained with nearly 1500 UAV flight data in the local area for 4 years. The importance coefficients of wind speed, wind direction, temperature, and humidity are 0.43, 0.31, 0.13, and 0.13 respectively. The ideal control period is set at 10 seconds, and it is calculated that the control period is reduced to 4.2 seconds at this time; the control period of the exploration area with medium complexity is 7.7 seconds, and the control period of the low complexity is 9.1 seconds.

[0031] S104 Construct a motion model of the UAV according to the attitude dynamics and position dynamics of the UAV. In each of the control periods, determine the optimal control sequence of the motion model of the UAV by solving the problems of energy efficiency optimization and flight stability, and obtain the motion trajectory of the UAV; It should be explained that the flight of the UAV is disturbed by various forces and environmental factors. The attitude dynamics covers the moment of inertia, aerodynamic moment, etc., which is related to the adjustment of the flight attitude. The position dynamics involves speed and acceleration, which determines the flight path. Solving the energy efficiency optimization is because the endurance affects the measurement range, and efficient flight can extend the operation time. Emphasizing flight stability is to avoid the UAV deviating from the flight path due to vibration and air flow impact, and ensure the accuracy of the measurement data. Only by combining the two can the optimal trajectory be planned; In the actual evaluation, the parameters of the drone are obtained, specifically: the moment of inertia is: 0.55 kg·m² in the x direction, 0.48 kg·m² in the y direction, and 0.41 kg·m² in the z direction; the aerodynamic parameters: the lift coefficient is 1.25, the drag coefficient is 0.88, the initial position is (650 m, 320 m, 200 m), the initial velocity is (3.5 m / s, 2 m / s, 1 m / s), and the acceleration is (0.7 m / s², 0.35 m / s², 0.17 m / s²). These are combined as state variables, and a state equation is constructed according to the dynamic variance and the state equation to obtain the motion model of the drone. In the actual evaluation, for exploration areas with high complexity, in each 4.2-second control cycle, the optimization problems of energy efficiency optimization and flight stability are solved. Through a multi-objective optimization algorithm and 40 iterations, the optimal control sequence is obtained, reducing the total energy consumption by 16% and the acceleration change rate by 21%, making the flight smoother.

[0032] S105 Correct and compensate the motion trajectory of the drone based on the electromagnetic interference received by the drone. In the actual evaluation, when the drone flies over a valley with high-voltage lines, the sensor measures that the peak value of the electromagnetic interference intensity is 58 dBμV / m, the frequency is 7 - 65 MHz multi-source, and the geomagnetic interference with chaotic phase. It is located as the high-voltage line and the valley communication base station, and the characteristics are analyzed. There are 2 periodic interference sources and 1 non-periodic one. A trajectory correction model is constructed to obtain the correction amount. Specifically, when approaching the interference source by 30 meters, the lateral correction is 3.2 meters and the longitudinal correction is 1.8 meters to avoid strong interference.

[0033] S106 Determine the structure of the drone's magnetometer array according to the accuracy requirements of the preset exploration area, and perform multi-drone formation based on the motion trajectory of the drone for magnetic field measurement. It should be noted that formation based on the drone's motion trajectory can ensure the collaborative operation of each drone, avoiding mutual interference and covering the area according to the plan, achieving non-overlooked magnetic field measurement and efficient data collection. In the actual evaluation, it is determined that the number of drones is 38. For the key suspected vein aggregation areas, each drone is equipped with 5 high-precision optically pumped magnetometers, which are closely arranged in a regular pentagon with the spacing accurately controlled at 0.32 meters to capture subtle magnetic field changes to the greatest extent. In general areas, each drone is configured with 3 ordinary magnetometers, arranged in an isosceles triangle with a spacing of 0.7 meters. The drones adopt an optimized distributed communication protocol with a communication frequency band of 2.4 GHz and a stable data transmission rate of 13 Mbps. When flying in formation, according to the preset movement trajectory of the drones, in the straight flight section, the spacing between adjacent drones is strictly maintained at 9 meters, and in the turning area, through a dynamic regulation algorithm, the spacing is expanded to 14 meters. Specifically, the number of drones in the high-complexity exploration area is 17, the number of drones in the medium-complexity exploration area is 13, and the number of drones in the low-complexity exploration area is 8, ensuring smooth flight and collaborative measurement effects.

[0034] S107 adjusts the multi-drone formation based on the magnetic field differences of different drones in the overlapping area.

[0035] It should be noted that due to the complex and variable underground geological structure and uneven magnetic field distribution in different regions, significant differences may exist even in adjacent positions. When multiple drones measure in the overlapping area, the magnetic field differences reflect underground anomalies. Adjusting the formation based on these differences can make subsequent measurements more refined, accurately capture subtle magnetic field changes, and improve the accuracy of geological structure inference. In the actual evaluation, during the measurement, the magnetic field data of each drone in the overlapping area is collected in real time. It is found that the magnetic field intensity difference between two adjacent drones in the overlapping area is as high as 63 nT, and the maximum direction deviation angle reaches 20°. It is confirmed as a geomagnetic gradient anomaly area, and the preset threshold of the geomagnetic gradient is 42 nT. The intelligent optimization algorithm is quickly enabled to increase the density of the drone formation by 40% proportionally within a range of 150 meters around the anomaly area, so that the measurement point density jumps from the original 62 points per square kilometer to 86.8 points. Then, this area is responsible for by the initial 13 drones, and the initial interval is reduced from 9 meters to 6.5 meters.

[0036] In this embodiment, the method for extracting the terrain features of the terrain and landform model and calculating the terrain complexity of the preset exploration area includes: Perform grid division on the terrain and landform model. For the elevation data in the terrain and landform model, use the differential method to calculate the terrain undulation, slope, and slope direction change rate. Perform a second-order difference operation on the elevation data to obtain the curvature of the terrain at different positions. The expression is:

[0037] Among them, is at The terrain curvature value at the is the elevation value at the position in the terrain and landform model, and are respectively the second-order partial derivatives in the direction and direction, is the mixed second-order partial derivative of and are respectively the first-order partial derivatives in the direction and direction, that is, the elevation change rate; Calculate the terrain complexity, and the expression is:

[0038] where is the terrain complexity at the position, and are respectively the number of grids in the row and column directions of the terrain and landform model, , , and are respectively the weight coefficients of terrain undulation, slope, aspect change rate, and terrain curvature, is the terrain undulation at the position, is the average value of terrain undulation, is the slope at the position, is the maximum slope among all positions, is the aspect change rate at the position, is the terrain curvature at the position, is the maximum terrain curvature among all positions.

[0039] In this embodiment, the method for performing regional clustering based on terrain complexity to obtain a set of exploration areas with different terrain complexities includes: Identify the points with a sharp change in the terrain complexity gradient as the clustering centers. Specifically, for each grid node center, select the nodes with the modulus of the terrain complexity greater than a preset threshold as the clustering centers; Calculate the similarity of the terrain complexity between each grid and the clustering centers. The expression is:

[0040] where Indicates the similarity of the terrain complexity between the grid at the position and the clustering center . is the sensitivity coefficient of the similarity function, and are respectively the terrain complexities at the position and the clustering center ; is the attenuation amplitude of the similarity function when the difference changes; Normalize the similarity from 0 to 1, and cluster the grids with a similarity greater than 0.7 with the clustering center into one category to obtain a set of exploration areas with different terrain complexities.

[0041] In this embodiment, the method for determining the control period by combining the gradient change of the meteorological characteristics of the combined area includes: Collect the meteorological factors of each exploration area in real time, including wind speed, wind direction, temperature and humidity; Construct a multi-layer perceptron model to fit the flight conditions of the unmanned aerial vehicle under different meteorological conditions, train the model with historical flight data, and determine the feature importance of each meteorological factor for normal flight; Obtain the gradient change rate of the meteorological characteristics, and calculate the control period according to the sensitivity coefficients of each meteorological factor. The expression is:

[0042] where is the control period of the preset unmanned aerial vehicle under ideal meteorological conditions, , , and are respectively the weight coefficients of wind speed, wind direction, temperature and humidity, , , and are respectively the collected values of wind speed, wind direction, temperature and humidity at the position, , , and are respectively the feature importances of wind speed, wind direction, temperature and humidity.

[0043] In this embodiment, the method for constructing the motion model of the unmanned aerial vehicle according to the attitude dynamics and position dynamics of the unmanned aerial vehicle includes: Establish the attitude dynamics equation describing the attitude change of the UAV and the position dynamics equation describing the position change of the UAV through an adaptive control algorithm; the attitude dynamics equation includes the inertial parameters, aerodynamic parameters, and control inputs of the UAV; the position dynamics equation includes the initial position, velocity, and acceleration of the UAV. Take the inertial parameters, aerodynamic parameters, initial position, velocity, and acceleration of the UAV as state variables and construct a state vector. Establish the state equation of the UAV based on the attitude dynamics equation and the position dynamics model, use the control input as the input of the state equation, describe the evolution process of the state vector over time, and obtain the motion model of the UAV.

[0044] In this embodiment, the method for determining the optimal control sequence of the motion model of the UAV and obtaining the motion trajectory of the UAV by solving the problems of energy efficiency optimization and flight stability includes: The objective function of energy efficiency optimization, the expression is:

[0045] Where is the control input of the UAV at time , is the number of sampling times within the control period, and are the times of the th and th sampling, and are the power consumption value and the task completion progress at time respectively, is the variance of the energy efficiency change, is the power consumption value at time ; The objective function of flight stability, the expression is:

[0046] Where is the flight time of the UAV, is the position vector of the UAV in space, is the weight coefficient of the acceleration change rate in the optimization objective, represents the acceleration vector, represents the acceleration change rate vector; Construct a multi-objective optimization problem by simultaneously minimizing the two objective functions, and use a multi-objective optimization algorithm to solve the optimal control sequence through iterative calculation; Apply the obtained optimal control sequence to the motion model of the UAV to obtain the flight trajectory.

[0047] In this embodiment, the method for correcting and compensating the motion trajectory of the drone based on the electromagnetic interference received by the drone includes: The drone sensor collects the electromagnetic interference intensity, frequency, and phase in real time, and locates the electromagnetic interference source in combination with the position of the drone; Perform feature analysis on the spectrum and time-domain characteristics of the electromagnetic interference source. According to the feature analysis results, construct an interference force function for the impact of electromagnetic interference on the drone flight, and the expression is:

[0048] where, is the number of periodic interference sources, is the th periodic interference source at time spectrum feature vector, is the number of non-periodic interference sources, is the th non-periodic interference source at time time-domain feature vector; Establish a model for correcting the drone flight trajectory in combination with the influence function, and the expression is:

[0049] where, is the mass of the drone, is the acceleration vector of the drone at time , is the gravitational acceleration vector, is the aerodynamic vector of the drone at time , is the thrust vector of the drone at time ; During the flight of the drone, based on the real-time collected electromagnetic interference data and the constructed correction model, calculate the correction amount required for the drone flight trajectory at the current moment, and correct the motion trajectory of the drone.

[0050] In this embodiment, the method for determining the structure of the magnetometer array of the drone according to the accuracy requirements of the preset exploration area and performing multi-drone formation based on the motion trajectory of the drone includes: Determine the number of drones that meet the coverage of the preset exploration area, and obtain the motion trajectory of each drone; According to the measurement accuracy requirements of the preset exploration area, determine the number and type of magnetometers carried on each drone, and the layout method of the magnetometers; Ensure the preset relative positions and flight synchronization among the drones, conduct multi-drone formation design, adopt a distributed communication protocol among the drones, and each drone exchanges motion states and geomagnetic gradient information with adjacent drones in real time to achieve collaborative measurement.

[0051] In this embodiment, the method for adjusting the multi-drone formation based on the magnetic field differences of different drones in the overlapping area includes: Collect the magnetic field data measured by each drone in the overlapping area of the preset exploration area; Calculate the magnetic field intensity difference and the magnetic field direction deviation angle, judge the positions where the magnetic field differences in the overlapping area show aggregation, divergence, and mutation, and obtain the geomagnetic gradient anomaly area; For the geomagnetic gradient anomaly area, when the geomagnetic gradient exceeds the gradient threshold, the formation density of the drones increases proportionally, increasing the measurement point density to capture the magnetic field changes.

[0052] The second aspect of the present invention also provides a drone airborne magnetic array measurement system, including: A terrain modeling module for scanning the three-dimensional terrain of the preset exploration area and constructing a terrain and landform model; A terrain feature clustering module for extracting the terrain features of the terrain and landform model, calculating the terrain complexity of the preset exploration area, and performing regional clustering based on the terrain complexity to obtain a set of exploration areas with different terrain complexities; A control period module for determining the control period in each exploration area in combination with the gradient change of the meteorological characteristics of the area; A trajectory control module for constructing a motion model of the drone according to the attitude dynamics and position dynamics of the drone, and determining the optimal control sequence of the motion model of the drone by solving the problems of energy efficiency optimization and flight stability in each control period to obtain the motion trajectory of the drone; An electromagnetic interference correction module for correcting and compensating the motion trajectory of the drone based on the electromagnetic interference received by the drone; A multi-drone formation module for determining the structure of the magnetometer array of the drones according to the accuracy requirements of the preset exploration area, and performing multi-drone formation based on the motion trajectory of the drones to conduct magnetic field measurement; A formation difference adjustment module for adjusting the multi-drone formation based on the magnetic field differences of different drones in the overlapping area.

[0053] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims of the present invention, they should fall within the protection scope of the present invention.

Claims

1. A drone aeromagnetic array measurement method, characterized in that: The following steps are involved: Scan the three-dimensional terrain of the preset exploration area and build a terrain model; Extracting the terrain features of the terrain model, calculating the terrain complexity of the preset exploration area, clustering the regions based on the terrain complexity, and obtaining a set of exploration regions with different terrain complexities; In each exploration area, the control period is determined in combination with the gradient changes in the regional meteorological characteristics; A motion model of the UAV is constructed according to the attitude dynamics and position dynamics of the UAV. In each control cycle, an optimal control sequence of the motion model of the UAV is determined by solving the problems of energy efficiency optimization and flight stability to obtain the motion trajectory of the UAV. Based on the electromagnetic interference to which the UAV is subjected, correcting and compensating the motion trajectory of the UAV; Determine the structure of the magnetometer array of the UAV according to the accuracy requirement of the preset exploration area, and form a multi-UAV formation based on the motion trajectory of the UAV to perform magnetic field measurement; The multi-UAV formation is adjusted according to the magnetic field differences of different UAVs in the overlapping areas.

2. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method of extracting the terrain features of the terrain model and calculating the terrain complexity of the preset exploration area includes: The terrain model is gridded, and the elevation data in the terrain model is used to calculate the terrain relief, slope, and slope change rate using the differential method; Perform a second-order difference operation on the elevation data to obtain the curvature of the terrain at different locations. The expression is: ； in, For The terrain curvature value at the location, For the topographic model The elevation value at the location, and They are exist Direction and The second-order partial derivative in the direction, for The mixed second-order partial derivatives of and They are exist Direction and The first partial derivative in direction, i.e. the rate of change of elevation; Calculate the terrain complexity, the expression is: ； in, For The complexity of the terrain at the location, and are the number of grids in the row and column directions of the terrain model, , , and are the weight coefficients of terrain relief, slope, slope change rate and terrain curvature, For The terrain relief at the location, is the average value of terrain relief, For The slope at the location, is the maximum slope among all positions, For The slope change rate at the location, For The curvature of the terrain at the location, is the maximum terrain curvature among all locations.

3. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method of clustering regions based on terrain complexity to obtain a set of exploration regions with different terrain complexity includes: Identify points where the terrain complexity gradient changes dramatically as cluster centers. Specifically, for each grid node center, select nodes whose terrain complexity modulus is greater than a preset threshold as cluster centers. Calculate the similarity of terrain complexity between each grid and the cluster center. The expression is: ； in, Indicated in Grid and cluster centers at locations The similarity of the terrain complexity, is the sensitivity coefficient of the similarity function, and Respectively in Location and cluster centers The complexity of the terrain, is the attenuation amplitude of the similarity function when the difference changes; The similarity is normalized from 0 to 1, and the grids with similarity greater than 0.7 to the cluster center are clustered into one category to obtain a set of exploration areas with different terrain complexity.

4. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method for determining the control period in combination with the gradient change of the meteorological characteristics of the region includes: Real-time collection of meteorological factors in each exploration area, including wind speed, wind direction, temperature and humidity; Construct a multi-layer perceptron model to fit the flight conditions of UAVs under different meteorological conditions, train the model using historical flight data, and determine the characteristic importance of each meteorological factor for normal flight; Obtain the gradient change rate of meteorological characteristics and calculate the control period according to the sensitivity coefficient of each meteorological factor. The expression is: ； in, Preset the control cycle of the drone under ideal weather conditions. , , and are the weight coefficients of wind speed, wind direction, temperature and humidity respectively, , , and Respectively in The collected values ​​of wind speed, wind direction, temperature and humidity at the location, , , and They are the feature importance of wind speed, wind direction, temperature and humidity respectively.

5. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method for constructing a motion model of a drone based on the attitude dynamics and position dynamics of the drone comprises: The attitude dynamics equation describing the attitude change of the UAV and the position dynamics equation describing the position change of the UAV are established through the adaptive control algorithm; the attitude dynamics equation includes the inertial parameters, aerodynamic parameters and control input of the UAV; the position dynamics equation includes the initial position, velocity and acceleration of the UAV; The inertial parameters, aerodynamic parameters, initial position, velocity and acceleration of the UAV are used as state variables to construct the state vector; The state equation of the UAV is established according to the attitude dynamics equation and the position dynamics model. The control input is used as the input of the state equation to describe the evolution of the state vector over time and obtain the motion model of the UAV.

6. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method of determining the optimal control sequence of the motion model of the UAV by solving the problems of energy efficiency optimization and flight stability and obtaining the motion trajectory of the UAV includes: The objective function of energy efficiency optimization is expressed as: ； in, For in time The control input of the UAV is is the number of sampling times in the control cycle, and For the Second and The sampling time, and Respectively at time The power consumption value and task completion progress at that time, is the variance of energy efficiency change, For in time The power consumption value when The objective function of flight stability is expressed as: ； in, is the flight time of the drone, is the position vector of the drone in space, is the weight coefficient of the acceleration change rate in the optimization objective, represents the acceleration vector, represents the rate of change vector of acceleration; The multi-objective optimization problem is constructed by minimizing two objective functions simultaneously, and the optimal control sequence is solved by iterative calculation using the multi-objective optimization algorithm; The solved optimal control sequence is applied to the UAV's motion model to obtain the flight trajectory.

7. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method for correcting and compensating the motion trajectory of the drone based on the electromagnetic interference to which the drone is subjected comprises: The drone sensor collects the electromagnetic interference intensity, frequency and phase in real time, and locates the electromagnetic interference source based on the drone’s position; The frequency spectrum and time domain characteristics of the electromagnetic interference source are analyzed. According to the characteristic analysis results, the interference force function of the electromagnetic interference on the UAV flight is constructed. The expression is: ； in, is the number of periodic interference sources, For the A periodic interference source at time The spectral feature vector at is the number of non-periodic interference sources, For the A non-periodic interference source is The time domain feature vector at ; Combined with the influence function, the model of UAV flight trajectory correction is established, and the expression is: ； in, For the quality of the drone, For drones in time The acceleration vector at time , is the gravitational acceleration vector, For drones in time The aerodynamic vector at For drones in time Thrust vector at ; During the flight of the UAV, based on the real-time collected electromagnetic interference data and the constructed correction model, the correction amount required for the UAV's flight trajectory at the current moment is calculated to correct the UAV's motion trajectory.

8. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method of determining the structure of the magnetometer array of the UAV according to the accuracy requirement of the preset exploration area, and performing multi-UAV formation based on the motion trajectory of the UAV, includes: Determine the number of drones required to cover the preset exploration area and obtain the movement trajectory of each drone; According to the measurement accuracy requirements of the preset exploration area, determine the number and type of magnetometers carried by each drone, as well as the layout of the magnetometers; Ensure the preset relative position and flight synchronization between UAVs, design a multi-UAV formation, use a distributed communication protocol between UAVs, and each UAV exchanges motion status and geomagnetic gradient information with adjacent UAVs in real time to achieve collaborative measurement.

9. The method for measuring the aeromagnetic array of an unmanned aerial vehicle according to claim 1, characterized in that: The method for adjusting the multi-UAV formation by using the magnetic field difference of different UAVs in the overlapping area includes: Collect magnetic field data measured by each drone in overlapping areas of the preset exploration area; Calculate the difference in magnetic field intensity and the deviation angle of magnetic field direction, determine the locations where magnetic field differences appear to gather, diverge and mutate in the overlapping areas, and obtain the geomagnetic gradient anomaly areas; For areas with abnormal geomagnetic gradient, when the geomagnetic gradient exceeds the gradient threshold, the density of the drone formation increases proportionally, increasing the density of measurement points to capture changes in the magnetic field.

10. An unmanned aerial vehicle aeromagnetic array measurement system, used to execute an unmanned aerial vehicle aeromagnetic array measurement method according to any one of claims 1 to 9, characterized in that: The system comprises: The terrain modeling module is used to scan the three-dimensional terrain of the preset exploration area and build a terrain model; A terrain feature clustering module, used to extract the terrain features of the terrain model, calculate the terrain complexity of the preset exploration area, perform regional clustering based on the terrain complexity, and obtain a set of exploration areas with different terrain complexities; A control cycle module is used to determine the control cycle in each exploration area in combination with the gradient changes of the meteorological characteristics of the area; The trajectory control module is used to construct a motion model of the UAV according to the attitude dynamics and position dynamics of the UAV, and in each control cycle, determine the optimal control sequence of the UAV's motion model by solving the problems of energy efficiency optimization and flight stability to obtain the UAV's motion trajectory; An electromagnetic interference correction module, used to correct and compensate the motion trajectory of the drone based on the electromagnetic interference to which the drone is subjected; A multi-UAV formation module is used to determine the structure of the magnetometer array of the UAV according to the accuracy requirements of the preset exploration area, and to form a multi-UAV formation based on the motion trajectory of the UAV to perform magnetic field measurement; The formation difference adjustment module is used to adjust the multi-UAV formation according to the magnetic field difference of different UAVs in the overlapping area.

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