Real-time preprocessing method for aerial magnetic measurement data of multi-rotor unmanned aerial vehicle

Abstract

The invention is suitable for the field of aeromagnetic systems of multi-rotor unmanned aerial vehicles, and provides a real-time preprocessing method for aeromagnetic survey data of a multi-rotor unmanned aerial vehicle, and the method comprises the steps: screening out data dead pixels: obtaining a cluster mean value from flight data A through a K-means algorithm, monitoring and screening whether the real-time flight data B meets the standard or not by using the obtained cluster mean value information, processing the data meeting the standard in the next step, and directly rejecting the data not meeting the standard; wherein the flight data A is a training set, and the flight data B is a test set; and eliminating the influence of the geomagnetic gradient: utilizing a formula for solving the geomagnetic gradient to carry out preprocessing of geomagnetic gradient interference field compensation so as to eliminate interference generated by the geomagnetic gradient.

Description

Technical Field

[0001] The present invention relates to the field of multi-rotor unmanned aerial vehicle (UAV) aeromagnetic systems, and in particular to a real-time preprocessing method for aeromagnetic survey data of a multi-rotor UAV. Background Art

[0002] Currently, multi-rotor drone aeromagnetic systems are widely used in practical aerial magnetic surveys. However, due to the limitations of the flight algorithm of multi-rotor drone platforms, which may cause unstable power supply voltage or excessive swing amplitude, the collected aeromagnetic data often contains some bad pixels and poor data quality. Moreover, because multi-rotor drones do not fly close to the ground during data collection, this also causes certain geomagnetic gradient interference in the collected magnetic survey data. In some cases where real-time aeromagnetic data compensation is required, if the collected data cannot be preprocessed quickly, efficiently, and accurately, the subsequent compensation effect will be affected. Therefore, real-time preprocessing of the collected data is necessary to ensure the accuracy of the subsequent compensation.

[0003] The traditional method for dealing with these bad pixels involves using multi-rotor drones to collect aeromagnetic data from the air. This data is then transmitted to the ground using onboard data transmission equipment. Manual removal of bad pixels from the magnetic data and the influence of geomagnetic gradient interference are then performed. While this method can remove bad pixels and reduce geomagnetic gradient interference, it cannot guarantee real-time data processing due to the significant time consumption associated with transmitting data and receiving and processing it on the ground. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to propose a real-time preprocessing method for multi-rotor UAV aerial magnetic survey data to solve the technical problems mentioned in the background technology.

[0005] A real-time preprocessing method for aeromagnetic survey data of a multi-rotor unmanned aerial vehicle, the method comprising: S1. Screening out bad data points: Use the K-means algorithm to obtain cluster means from flight data A. Use the obtained cluster mean information to monitor and screen whether the real-time flight data B meets the standards. Data that meets the standards will enter the next step of processing, and data that does not meet the standards will be directly eliminated. Flight data A is the training set, and flight data B is the test set. S2. Eliminate the influence of geomagnetic gradient: Use the existing formula for solving geomagnetic gradient to perform preprocessing of geomagnetic gradient interference field compensation to eliminate the interference caused by geomagnetic gradient on the magnetic survey data of multi-rotor drones.

[0006] Preferably, in step S1, the K-means algorithm usually uses Euclidean distance to measure the distance d from the point to the cluster center, and the goal is to minimize the sum of squared errors within the cluster. The principle formula is: ; K is the number of clusters, is the aeromagnetic data set of cluster i, and x is the The aeromagnetic data, is the centroid of the ith cluster, Represents aeromagnetic data and centroid The square of the Euclidean distance; Here, the aeromagnetic data of the training set are first filtered, and the number of cluster centers is set. Then, the nearest cluster center is found for each filtered aeromagnetic data. , and update the center of each cluster is the mean of all points in the cluster, and the cluster mean of the aeromagnetic data of this area is obtained by the following formula: .

[0007] Preferably, the content of step S2 includes: The formula for geomagnetic gradient interference is: ; The geomagnetic gradient interference, is the geomagnetic field value, R is the average radius of the earth, h is the actual flight altitude, Ground base point elevation; Substitute the calculated geomagnetic gradient interference into the following formula for calculation, and the result is the total geomagnetic field Magnetic survey data without the influence of geomagnetic gradient : ; The magnetic interference value of the drone is obtained by removing the geomagnetic background field value H from the magnetic survey data after removing the influence of the geomagnetic gradient. : ; Therefore, the multi-rotor UAV aerial magnetic survey data after the first step of screening and processing can be used to eliminate the influence of geomagnetic gradient using the above formula.

[0008] Beneficial effects achieved by the present invention: Compared with the traditional method of transmitting aeromagnetic data back to the ground and having ground personnel perform pre-processing operations, this method can directly complete the calculation on the computing platform carried by the multi-rotor UAV aeromagnetic survey platform, screen out bad points and remove geomagnetic gradient interference, which can greatly shorten the data pre-processing time and improve the efficiency of obtaining valid data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is the FOM flight track diagram in an embodiment of the present invention.

[0010] Figure 2 The figure is a flow chart of a real-time preprocessing method for multi-rotor UAV aeromagnetic data. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0012] See also Figures 1 to 2 The embodiment of the present invention provides a real-time preprocessing method for aeromagnetic survey data of a multi-rotor UAV, the method comprising: S1. Eliminate bad data points: Use the K-means algorithm to obtain the cluster mean from flight data A. Use the obtained cluster mean information to monitor and screen whether the real-time flight data B meets the standards. The data that meets the standards will enter the next step of processing, and the data that does not meet the standards will be directly eliminated. Among them, flight data A is the training set, and flight data B is the test set. In this embodiment, a flight specification designed by Leliak, Figure of Merit (FOM) flight, is used to obtain data. When performing FOM flight, as shown in Figure ( Figure 1 ), the aircraft needs to perform three ±10° roll, ±5° pitch, and ±5° yaw maneuvers along the four headings of north, east, south, and west respectively, with each attitude lasting 5 to 10 seconds.

[0013] S2. Eliminate the influence of geomagnetic gradient: Use the existing formula for solving geomagnetic gradient to perform preprocessing of geomagnetic gradient interference field compensation to eliminate the interference caused by geomagnetic gradient on the magnetic survey data of multi-rotor drones.

[0014] In step S1 of this embodiment, the K-means algorithm generally uses the Euclidean distance to measure the distance d from a point to the cluster center. The goal is to minimize the sum of squared errors within the cluster. The principle formula is: ; K is the number of clusters, is the aeromagnetic data set of cluster i, and x is the The aeromagnetic data, is the centroid of the ith cluster, Represents aeromagnetic data and centroid The square of the Euclidean distance; Here, the aeromagnetic data of the training set are first filtered, and the number of cluster centers is set. Then, the nearest cluster center is found for each filtered aeromagnetic data. , and update the center of each cluster is the mean of all points in the cluster, and the cluster mean of the aeromagnetic data of this area is obtained by the following formula: .

[0015] The contents of step S2 in this embodiment include: The formula for geomagnetic gradient interference is: ; The geomagnetic gradient interference, is the geomagnetic field value, R is the average radius of the earth, h is the actual flight altitude, Ground base point elevation; Substitute the calculated geomagnetic gradient interference into the following formula for calculation, and the result is the total geomagnetic field Magnetic survey data without the influence of geomagnetic gradient : ; The magnetic interference value of the drone is obtained by removing the geomagnetic background field value H from the magnetic survey data after removing the influence of the geomagnetic gradient. : ; Therefore, the multi-rotor UAV aerial magnetic survey data after the first step of screening and processing can be used to eliminate the influence of geomagnetic gradient using the above formula.

[0016] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the description of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A real-time preprocessing method for multi-rotor UAV aeromagnetic data, characterized in that: The method comprises: S1. Screening out bad data points: Use the K-means algorithm to obtain cluster means from flight data A. Use the obtained cluster mean information to monitor and screen whether the real-time flight data B meets the standards. Data that meets the standards will enter the next step of processing, and data that does not meet the standards will be directly eliminated. Flight data A is the training set, and flight data B is the test set. S2. Eliminate the influence of geomagnetic gradient: Use the formula for solving geomagnetic gradient to perform preprocessing of geomagnetic gradient interference field compensation to eliminate the interference caused by geomagnetic gradient.

2. The real-time preprocessing method for aeromagnetic survey data of a multi-rotor UAV according to claim 1, characterized in that: In step S1, the K-means algorithm uses Euclidean distance to measure the distance d from a point to the cluster center. The goal is to minimize the sum of squared errors within the cluster. The principle formula is: ； K is the number of clusters, is the aeromagnetic data set of cluster i, and x is the The aeromagnetic data, is the centroid of the ith cluster, Represents aeromagnetic data and centroid The square of the Euclidean distance; First, filter the aeromagnetic data of the training set and set the number of cluster centers. Then, find the nearest cluster center for each filtered aeromagnetic data. , and update the center of each cluster is the mean of all points in the cluster, and the cluster mean of the aeromagnetic data of this area is obtained by the following formula: 。 3. The real-time preprocessing method for aeromagnetic survey data of a multi-rotor UAV according to claim 1, characterized in that: The contents of step S2 include: The formula for geomagnetic gradient interference is: ； The geomagnetic gradient interference, is the geomagnetic field value, R is the average radius of the earth, h is the actual flight altitude, Ground base point elevation; Substitute the calculated geomagnetic gradient interference into the following formula for calculation, and the result is the total geomagnetic field Magnetic survey data without the influence of geomagnetic gradient : ； The magnetic interference value of the drone is obtained by removing the geomagnetic background field value H from the magnetic survey data after removing the influence of the geomagnetic gradient. : ； Therefore, the filtered and processed multi-rotor UAV aerial magnetic survey data can be used to eliminate the influence of geomagnetic gradient using the above formula.

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