Data compensation method, device, electronic device and storage medium

Abstract

The embodiments of the present application provide a data compensation method, device, electronic device and storage medium. The electronic device first obtains full tensor magnetic gradient data; then, preprocesses the full tensor magnetic gradient data to determine first magnetic data; and determines compensation parameters for each segment of magnetic data in the first magnetic data; compensates each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine each segment of compensated magnetic gradient data; finally, based on each segment of compensated magnetic gradient data, determines final magnetic gradient compensation data, thereby achieving accurate compensation of low-altitude full tensor magnetic gradient data.

Description

Technical Field

[0001] The present application relates to the field of aeromagnetic measurement technology, and relates to but is not limited to a data compensation method, device, electronic equipment and storage medium. Background Art

[0002] The traditional magnetic compensation method is mainly a high-altitude compensation method, that is, the compensation parameters are calculated using high-altitude magnetic data, and then the compensation parameters are used to compensate the low-altitude magnetic gradient data. However, this method has its limitations for the compensation of superconducting full-tensor magnetic gradient data for the following reasons: First, the superconducting magnetic gradiometer is mostly installed in a pod, connected to the aircraft by a tow cable for aeromagnetic survey, but the pod cannot complete various flight movements according to the needs of high-altitude compensation flight, so the actual effect of the traditional high-altitude compensation method is not ideal; secondly, the superconducting magnetic gradiometer is sensitive to radio frequency interference. In high-altitude compensation flight, radio frequency interference cannot be ignored, resulting in poor high-altitude compensation flight effect; finally, it is difficult to complete high-altitude compensation flight in special flight areas such as civil airliner take-off and landing, military flight training, etc.

[0003] That is, the traditional high-altitude compensation method increases the flight cost and difficulty of the superconducting full-tensor magnetic measurement system, resulting in poor compensation accuracy of low-altitude full-tensor magnetic gradient data. Summary of the invention

[0004] The embodiments of the present application provide a data compensation method, device, electronic device and storage medium, which can effectively improve the compensation accuracy of magnetic gradient data.

[0005] The technical solution of this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a data compensation method, the method comprising:

[0007] Acquire full tensor magnetic gradient data;

[0008] Preprocessing the full tensor magnetic gradient data to determine first magnetic data;

[0009] determining a compensation parameter for each segment of magnetic data in the first magnetic data;

[0010] Each segment of magnetic data is compensated based on the compensation parameter of each segment of magnetic data to determine compensated magnetic gradient data.

[0011] In a second aspect, an embodiment of the present application provides a data compensation device, the device comprising:

[0012] An acquisition unit, used for acquiring full tensor magnetic gradient data;

[0013] A processing unit, configured to pre-process the full tensor magnetic gradient data to determine first magnetic data;

[0014] a determining unit, configured to determine a compensation parameter for each segment of magnetic data in the first magnetic data;

[0015] The compensation unit is used to compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine compensated magnetic gradient data.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory; wherein:

[0017] The memory is used to store computer programs;

[0018] The processor is used to call and run the computer program stored in the memory to execute the data compensation method as described above.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the data compensation method as described above is implemented.

[0020] The embodiment of the present application provides a data compensation method, device, electronic device and storage medium, wherein the electronic device first obtains full tensor magnetic gradient data; then, preprocesses the full tensor magnetic gradient data to determine the first magnetic data; and determines the compensation parameters of each segment of magnetic data in the first magnetic data; finally, based on the compensation parameters of each segment of magnetic data, each segment of magnetic data is compensated to determine the compensated magnetic gradient data. It can be seen that in the embodiment of the present application, the first magnetic data is obtained by preprocessing the full tensor magnetic gradient data, and the compensation parameters of each segment of magnetic data in the first magnetic data are further obtained, so that each segment of magnetic data can be compensated according to the compensation parameters of each segment of magnetic data to obtain the compensated magnetic data, thereby realizing accurate compensation of low-altitude full tensor magnetic gradient data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 1 ;

[0023] Figure 2 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 2 ;

[0024] Figure 3 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 3 ;

[0025] Figure 4 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 4 ;

[0026] Figure 5 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 5 ;

[0027] Figure 6 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 6 ;

[0028] Figure 7 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 7 ;

[0029] Figure 8 A flight path diagram of an aircraft flying in a pod as proposed in an embodiment of the present application;

[0030] Fig. 9 This is a track diagram after the survey line is cut as proposed in the embodiment of the present application;

[0031] Fig.10 A schematic diagram showing the comparison of the amplitudes of magnetic data before and after processing proposed in an embodiment of the present application;

[0032] Fig.11 A schematic diagram showing the comparison of the gradient values ​​of the original magnetic gradient data proposed in the embodiment of the present application, the magnetic gradient data after conventional high-altitude compensation, and the magnetic gradient data after compensation in the present application;

[0033] Fig.12 A schematic diagram showing the comparison of Bzz components of two repeated line gradient data after compensation proposed in an embodiment of the present application;

[0034] Fig.13 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 8 ;

[0035] Fig.14 A schematic diagram of comparison between the first magnetic data and the yaw angle dynamic data proposed in an embodiment of the present application;

[0036] Fig.15A schematic diagram of the structure of a data compensation device proposed in an embodiment of the present application;

[0037] Fig.16 A schematic diagram of the structure of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. It is understood that the specific embodiments described herein are only used to explain the related applications, rather than to limit the applications. It should also be noted that, for ease of description, only the parts related to the related applications are shown in the drawings.

[0039] Aeromagnetic survey technology has the characteristics of high detection efficiency, small surface magnetic interference, and low operating cost per unit area. It is one of the important means to conduct resource surveys and screen prospecting targets. In recent years, thanks to the vigorous development of SQUID (Superconducting Quantum Interference Device) magnetic gradient measurement systems, it has become possible to directly measure high-precision magnetic gradient data. Superconducting full tensor magnetic gradient meters are considered to be the development direction of the third generation of aeromagnetic measurement due to their many advantages such as high information content, high sensitivity, wide bandwidth and high slew rate. As one of the most cutting-edge technologies in international aeromagnetic survey, the research and development of low-temperature super-navigation magnetic full tensor magnetic gradient measurement systems is difficult. In recent years, with the hard work of a large number of scientific workers, China has also developed a prototype of a low-temperature superconducting full tensor magnetic gradient measurement system and completed field flight experiments. However, due to the late start of super-navigation aeromagnetic survey technology, there is currently no complete set of compensation processing methods for superconducting full tensor magnetic gradient data.

[0040] At present, the traditional magnetic compensation method is mainly the high-altitude compensation method, that is, the compensation parameters are calculated using the high-altitude magnetic data, and then the compensation parameters are used to compensate the low-altitude survey line data. However, this method has its limitations for the compensation of superconducting full-tensor magnetic gradient data. The reasons are as follows: First, the superconducting magnetic gradient meter is mostly installed in the pod, connected to the aircraft through the tow cable for aeromagnetic survey, but the pod cannot complete various flight movements according to the needs of high-altitude compensation flight, so the actual effect of the traditional high-altitude compensation method is not ideal; secondly, the superconducting magnetic gradient meter is sensitive to radio frequency interference. In the high-altitude compensation flight, radio frequency interference cannot be ignored, resulting in poor high-altitude compensation flight effect; finally, it is difficult to complete high-altitude compensation flight in special flight areas such as civil airliner take-off and landing, military flight training, etc. That is, the traditional high-altitude compensation method increases the flight cost and difficulty of the superconducting full-tensor magnetic measurement system, resulting in poor compensation accuracy of magnetic gradient data.

[0041] In this regard, the present application proposes a data compensation method, which does not require high-altitude compensation flight, can directly compensate for low-altitude survey line data, and realize segmented compensation of magnetic data, thereby improving the compensation accuracy of magnetic data.

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0043] The embodiment of the present application provides a data compensation method. It should be noted that the data compensation method proposed in the embodiment of the present application can be applied to a data compensation device or an electronic device. This application does not make any specific limitation on this.

[0044] Furthermore, in the embodiments of the present application, the data compensation method proposed in the embodiments of the present application is exemplarily described by taking an electronic device as an example. Figure 1 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 1 ,like Figure 1 As shown, the data compensation method may specifically include the following steps:

[0045] Step 101: Acquire full tensor magnetic gradient data.

[0046] In an embodiment of the present application, the electronic device can acquire full tensor magnetic gradient data.

[0047] It should be noted that, in the embodiments of the present application, the execution subject of the data compensation method may be an electronic device, and the electronic device may be a variety of types of devices, for example, the electronic device may be any form of device including but not limited to a laptop computer.

[0048] It should be noted that, in the embodiments of the present application, the full tensor magnetic gradient data may be low spatial domain magnetic gradient data.

[0049] Step 102: pre-process the full tensor magnetic gradient data to determine first magnetic data.

[0050] In an embodiment of the present application, after acquiring the full tensor magnetic gradient data, the electronic device may further preprocess the full tensor magnetic gradient data to determine the first magnetic data.

[0051] It should be noted that in the embodiments of the present application, the preprocessing includes but is not limited to line cutting processing, invalid abnormal automatic removal processing and downsampling processing. The first magnetic data is the magnetic gradient data obtained after the electronic device sequentially performs line cutting processing, invalid abnormal automatic removal processing and downsampling processing on the full tensor magnetic gradient data.

[0052] It should be noted that in the embodiments of the present application, since the flight track of the aircraft in the low altitude area is winding, it is difficult to uniformly process the full tensor magnetic gradient data collected based on the flight track. Based on this, after the full tensor magnetic gradient data is cut into survey line data, it can be processed into survey line data that is easy to process; since there are some invalid abnormal data in the survey line data, in order not to affect the accuracy of the compensation result, the invalid abnormal data in the survey line data is further removed to ensure that the remaining second magnetic data and inertial navigation data are valid data; since the data lengths of the second magnetic data and the inertial navigation data are inconsistent, the second magnetic data needs to be further downsampled so that the first magnetic data with the same data length can be obtained.

[0053] Step 103: Determine compensation parameters for each segment of magnetic data in the first magnetic data.

[0054] In an embodiment of the present application, after preprocessing the full tensor magnetic gradient data to determine the first magnetic data, the electronic device may further determine compensation parameters for each segment of magnetic data in the first magnetic data.

[0055] It should be noted that, in the embodiments of the present application, the compensation parameters of each segment of magnetic data can be used to perform compensation processing on each segment of magnetic data respectively. The two compensation parameters of any two different segments of magnetic data can be the same or different, and the present application does not specifically limit this.

[0056] It should be noted that in an embodiment of the present application, in order to determine each segment of magnetic data in the first magnetic data, the yaw angle dynamic data can be first obtained, and then the mutation position thereof can be determined based on the yaw angle dynamic data; based on the mutation position, the position to be segmented corresponding to the first magnetic data can be further determined; finally, the first magnetic data can be segmented based on the position to be segmented, so as to determine each segment of magnetic data in the first magnetic data.

[0057] It should be noted that in the embodiment of the present application, after each segment of magnetic data is determined, a corresponding magnetic compensation target function can be established based on each segment of magnetic data, and then based on the magnetic compensation target function, the compensation parameter of each segment of magnetic data can be further determined. In other words, the compensation parameter of any segment of magnetic data is determined based on the magnetic compensation target function.

[0058] It should be noted that, in the embodiments of the present application, the magnetic compensation target function corresponding to any segment of magnetic data is established based on the coefficient matrix corresponding to the segment of magnetic data and the segment of magnetic data.

[0059] It should be noted that, in the embodiment of the present application, the magnetic compensation objective function of each segment of magnetic data can be defined according to the following formula:

[0060]

[0061] Among them, A j is the coefficient matrix of the j-th segment magnetic gradient data, X j is the compensation coefficient of the j-th segment magnetic gradient data, b j is the j-th segment magnetic gradient data.

[0062] It should be noted that, in the embodiment of the present application, the compensation coefficient of each segment of magnetic data can be defined according to the following formula:

[0063]

[0064] in, is the transposed matrix of the coefficient matrix of the j-th segment magnetic gradient data, λ j is the error estimate, I is the diagonal unit matrix, X jλ is the compensation coefficient of the j-th segment magnetic data calculated using the ridge regression algorithm.

[0065] Step 104 : Compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine compensated magnetic gradient data.

[0066] In an embodiment of the present application, after determining the compensation parameters of each segment of magnetic data in the first magnetic data, the electronic device may further compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine compensated magnetic gradient data.

[0067] It should be noted that, in the embodiments of the present application, the compensation parameters of any segment of magnetic data only perform targeted compensation on the segment of magnetic data, that is, the present application adopts a segmented compensation method to improve the compensation accuracy of the magnetic data.

[0068] It should be noted that in the embodiments of the present application, after determining any segment of magnetic data, a full tensor magnetic gradient compensation model can be established first, and then the compensation parameters of the segment of magnetic data and the segment of magnetic data are input into the full tensor magnetic gradient compensation model to determine the compensated magnetic data, and each segment of compensated magnetic data is spliced ​​to determine the compensated magnetic gradient data.

[0069] It should be noted that, in the embodiment of the present application, the process of establishing a full tensor magnetic gradient compensation model is specifically as follows: a first magnetic compensation model is established based on the internal noise of the sensor, and a second magnetic compensation model is established based on the external noise of the sensor; and then a full tensor magnetic gradient compensation model is established based on the first magnetic compensation model and the second magnetic compensation model.

[0070] It should be noted that, in the embodiments of the present application, the internal noise of the sensor may include noise caused by differences in sensitivity of multiple sensors, noise caused by non-orthogonality of the three axes of the sensor or installation errors, and imbalance interference.

[0071] It should be noted that, in the embodiments of the present application, the sensor external noise may include residual magnetism / permanent magnetism, inductive magnetism and eddy current magnetic field.

[0072] It should be noted that, in the embodiment of the present application, the full tensor magnetic gradient compensation model of a single channel can be defined according to the following formula:

[0073]

[0074] Where i is the measurement channel number, t is the time, M i is the compensated magnetic gradient data of the i-th channel, is the measured magnetic gradient data of the i-th channel, T2 is the transformation matrix from the earth coordinate system to the pod coordinate system, M r is the coefficient of variation, H e is the background magnetic field in the geodetic coordinate system, k ind =(k ind_x ,k ind_y ,k ind_z ) is the magnetic interference coefficient of the external interference source, e v =(e v_x ,e v_y ,e v_z ) is the eddy current interference coefficient of the external interference source, B r =(B rx ,B ry ,B rz ) is the magnetometer value after compensation, (d x ,d y ,d z ) is the imbalance interference coefficient, n is the high-frequency interference, A is the coefficient matrix, and X is the compensation coefficient.

[0075] It should be noted that, in the embodiment of the present application, the coefficient matrix can be defined according to the following formula:

[0076]

[0077] Where N is the total number of points measuring magnetic gradient data.

[0078] It should be noted that, in the embodiment of the present application, the compensation coefficient can be defined according to the following formula:

[0079] X=(M r ,k ind_x ,k ind_y ,k ind_z ,ev_x ,e v_y ,e v_z ,d x ,d y ,d z ,) T (5)

[0080] It should be noted that, in the embodiment of the present application, the full tensor magnetic gradient compensation model of a single channel takes into account the external noise and internal noise (imbalance interference) of the magnetic gradient sensor.

[0081] It should be noted that, in the embodiment of the present application, each segment of magnetic data can be defined according to the following formula:

[0082]

[0083] in, is the measured value of the j-th segment magnetic gradient data in the i-th channel, is the magnetic gradient data after compensation for the jth segment in the i-th channel.

[0084] It should be noted that, in the embodiment of the present application, the compensated magnetic gradient data can be defined according to the following formula:

[0085]

[0086] Among them, M i are all compensated magnetic gradient data in the i-th channel.

[0087] Further, Figure 2 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 2 ,like Figure 2 As shown, after compensating each segment of magnetic data based on the compensation parameters of each segment of magnetic data and determining the compensated magnetic gradient data, that is, after step 104, the data compensation method may further include the following steps:

[0088] Step 105: filter the compensated magnetic gradient data to remove high frequency noise, and obtain filtered magnetic gradient data.

[0089] In an embodiment of the present application, after compensating each segment of magnetic data based on the compensation parameters of each segment of magnetic data and determining the compensated magnetic gradient data, the electronic device can further perform high-frequency noise filtering on the compensated magnetic gradient data to obtain filtered magnetic gradient data.

[0090] It should be noted that, in the embodiment of the present application, the compensated magnetic gradient data still contains some high-frequency noise that cannot be filtered out, so it is necessary to further filter the compensated magnetic gradient data according to the signal frequency band characteristics of the actual application problem.

[0091] It should be noted that, in the embodiment of the present application, the electronic device may use low-pass filtering to filter out high-frequency noise in the compensated magnetic gradient data.

[0092] It should be noted that, in the embodiments of the present application, the interference in the compensated magnetic gradient data mainly comes from the magnetic anomalies caused by the lithosphere.

[0093] It should be noted that, in the embodiments of the present application, the compensation effect can be evaluated based on the compensation improvement ratio and the root mean square of the full tensor magnetic gradient data and the filtered magnetic gradient data, respectively.

[0094] It should be noted that, in the embodiments of the present application, the compensation improvement ratio represents the ratio of the standard deviation of the full tensor magnetic gradient data to the filtered magnetic gradient data. Generally, the larger the compensation improvement ratio, the better the compensation effect.

[0095] It should be noted that, in the embodiment of the present application, the compensation improvement ratio can be defined according to the following formula:

[0096]

[0097] Wherein, N is the total number of points for measuring magnetic gradient data; l is the serial number of magnetic gradient data, l = 1, 2, ..., N; is the average value of the compensated magnetic gradient data; is the lth magnetic gradient data after compensation; is the average value of the original data; is the lth full tensor magnetic gradient data; σ c0 is the standard deviation of the full tensor magnetic gradient data; σ c is the standard deviation of the compensated magnetic gradient data.

[0098] It should be noted that in the embodiments of the present application, the root mean square represents the effective value of the data. Generally, the smaller the effective value of the filtered magnetic gradient data is, the better the compensation effect is.

[0099] It should be noted that, in the embodiments of the present application, the root mean square can be defined according to the following formula:

[0100]

[0101] in, is the square value of the lth magnetic gradient data after compensation.

[0102] Further, Figure 3 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 3 ,like Figure 3 As shown, the method for preprocessing the full tensor magnetic gradient data to determine the first magnetic data specifically includes the following steps:

[0103] Step 1021: perform line cutting on the full tensor magnetic gradient data to obtain line data.

[0104] In an embodiment of the present application, after acquiring the full tensor magnetic gradient data, the electronic device may further perform survey line cutting on the full tensor magnetic gradient data to obtain survey line data.

[0105] It should be noted that, in an embodiment of the present application, the flight direction of the survey line is determined according to the longitude and latitude data of the inertial navigation system in the low-temperature super-navigation space full tensor magnetic gradient measurement system, and a common starting point and an ending point are determined based on parallel survey lines in the same direction. The full tensor magnetic gradient data is cut according to the coordinate data of the starting point and the ending point to obtain the cut full tensor magnetic gradient data, i.e., the survey line data, wherein liquid helium provides a low-temperature environment of 4.2K for the super-navigation space full tensor magnetic gradient measurement system.

[0106] Step 1022: remove abnormal data in the survey line data to obtain second magnetic data and inertial navigation data.

[0107] In an embodiment of the present application, after performing line cutting on the full tensor magnetic gradient data to obtain line data, the electronic device may further remove abnormal data in the line data to obtain second magnetic data and inertial navigation data.

[0108] It should be noted that, in the embodiments of the present application, the invalid abnormal data in the measurement line data can represent the data of the entire process from the loss of lock to the reset of the superconducting chip in the superconducting full tensor magnetic measurement system.

[0109] It should be noted that in the embodiments of the present application, under the influence of high-altitude airflow, the pod in the superconducting full tensor magnetic measurement system may produce severe attitude vibration, which will cause large fluctuations in the measurement line data. At this time, the collected signal amplitude will fluctuate violently within the abnormal range. Therefore, the invalid abnormal data in the measurement line data can also represent data with violent fluctuations in the signal amplitude within the abnormal range.

[0110] It should be noted that, in the embodiments of the present application, the electronic device may use a sliding window moving variance method to automatically remove invalid anomalies from the survey line data.

[0111] It should be noted that, in the embodiment of the present application, taking one channel as an example, the sliding window moving variance method is specifically as follows: Assuming that the original full tensor magnetic gradient data of a survey line is {P1, ..., P N-1 , P N}, where N is the total number of data points on the survey line; calculate the moving variance value of all data on the survey line at a certain step length (step) to obtain the variance sequence {Q1, Q2…, Qm}; then calculate the average value of the variance sequence; finally remove the n data before and after the value greater than k times the average value in the variance sequence. Where k and n can be set according to actual conditions.

[0112] It should be noted that, in an embodiment of the present application, the electronic device can synchronously remove abnormal data in the remaining channels of the superconducting full tensor magnetic measurement system based on the sliding window moving variance method to maintain the synchronization of multi-channel data.

[0113] Step 1023: downsample the second magnetic data to obtain first magnetic data having the same length as the inertial navigation data.

[0114] In an embodiment of the present application, after removing abnormal data in the survey line data to obtain the second magnetic data and the inertial navigation data, the electronic device may further downsample the second magnetic data to obtain the first magnetic data having the same length as the inertial navigation data.

[0115] It should be noted that, in the embodiment of the present application, the sampling rates of the second magnetic data and the inertial navigation data are different, that is, the frequency of collecting the second magnetic data is much greater than the frequency of collecting the inertial navigation data.

[0116] Exemplarily, the frequency of the second magnetic data acquisition and the frequency of the inertial navigation data acquisition may both be any value greater than 0. For example, the frequency of the second magnetic data acquisition may be 1000 Hz, and the frequency of the inertial navigation data acquisition may be 10 Hz.

[0117] It should be noted that in the embodiment of the present application, the electronic device can use the mean sampling method to downsample the second magnetic data to obtain the first magnetic data and the inertial navigation data with the same data length, so as to well preserve the information in the magnetic signal.

[0118] Further, Figure 4 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 4 ,like Figure 4 As shown, after preprocessing the full tensor magnetic gradient data to determine the first magnetic data, that is, after step 102, and before determining the compensation parameters of each segment of magnetic data in the first magnetic data, that is, before step 103, the data compensation method may further include the following steps:

[0119] Step 106: Acquire yaw angle dynamic data.

[0120] In an embodiment of the present application, after preprocessing the full tensor magnetic gradient data and determining the first magnetic data, the electronic device can further acquire yaw angle dynamic data.

[0121] It should be noted that, in the embodiment of the present application, the yaw angle dynamic data and the first magnetic data are data collected at the same time and at the same position.

[0122] Step 107: Determine the mutation position according to the yaw angle dynamic data.

[0123] In an embodiment of the present application, after acquiring the yaw angle dynamic data, the electronic device may further determine the mutation position according to the yaw angle dynamic data.

[0124] It should be noted that, in the embodiment of the present application, the mutation position represents the position where the yaw angle dynamic data undergoes a mutation.

[0125] Step 108: Determine the to-be-segmented position corresponding to the first magnetic data based on the mutation position.

[0126] In an embodiment of the present application, after determining the mutation position according to the yaw angle dynamic data, the electronic device may further determine the to-be-segmented position corresponding to the first magnetic data based on the mutation position.

[0127] It should be noted that, in the embodiment of the present application, since the yaw angle dynamic data and the first magnetic data are collected at the same time and the same position, the position where the yaw angle dynamic data suddenly changes also corresponds to the position where the first magnetic data suddenly changes.

[0128] It should be noted that, in the embodiment of the present application, the position to be segmented represents the position where the first magnetic data has a sudden change.

[0129] Step 109: segment the first magnetic data based on the position to be segmented, and determine each segment of magnetic data.

[0130] In an embodiment of the present application, after determining the to-be-segmented position corresponding to the first magnetic data based on the mutation position, the electronic device may further segment the first magnetic data based on the to-be-segmented position to determine each segment of magnetic data.

[0131] It should be noted that, in the embodiments of the present application, each segment of magnetic data is data from which mutation data is eliminated, that is, each segment of magnetic data is relatively stable data.

[0132] Further, Figure 5 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 5 ,like Figure 5As shown, the method for determining the compensation parameter of each segment of magnetic data in the first magnetic data specifically includes the following steps:

[0133] Step 1031: Establish a magnetic compensation target function for each segment of magnetic data.

[0134] In an embodiment of the present application, after segmenting the first magnetic data based on the position to be segmented and determining each segment of magnetic data, the electronic device may further establish a magnetic compensation target function for each segment of magnetic data.

[0135] It should be noted that, in the embodiments of the present application, the magnetic compensation target function of each segment of magnetic data may be different, partially the same, or partially different, and the present application does not make any specific limitation on this.

[0136] It should be noted that, in the embodiment of the present application, the magnetic compensation target function of each segment of magnetic data can be defined according to the above formula (1).

[0137] Step 1032: Determine compensation parameters for each segment of magnetic data based on the magnetic compensation objective function.

[0138] In an embodiment of the present application, after establishing the magnetic compensation target function for each segment of magnetic data, the electronic device may further determine the compensation parameters for each segment of magnetic data based on the magnetic compensation target function.

[0139] It should be noted that, in the embodiment of the present application, the compensation coefficient of each segment of magnetic data can be defined according to the above formula (2).

[0140] It should be noted that in the embodiment of the present application, the electronic device can use the ridge regression algorithm to solve the compensation coefficient of each segment of magnetic data through the magnetic compensation objective function of each segment of magnetic data. In this way, the complex collinearity problem of the full tensor magnetic gradient compensation model can be well solved.

[0141] Further, Figure 6 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 6 ,like Figure 6 As shown, the method for establishing the magnetic compensation target function for each segment of magnetic data specifically includes the following steps:

[0142] Step 1031a: determine the coefficient matrix corresponding to each segment of magnetic data.

[0143] In an embodiment of the present application, after segmenting the first magnetic data based on the position to be segmented and determining each segment of magnetic data, the electronic device may further determine a coefficient matrix corresponding to each segment of magnetic data.

[0144] It should be noted that, in the embodiment of the present application, the coefficient matrix corresponding to each segment of magnetic data is determined by the full tensor magnetic gradient compensation model.

[0145] It should be noted that, in the embodiments of the present application, the coefficient matrices corresponding to each segment of magnetic data may be different, partially the same, or partially different, and the present application does not make any specific limitation on this.

[0146] Step 1031b: establishing a magnetic compensation target function according to the coefficient matrix corresponding to each segment of magnetic data and each segment of magnetic data.

[0147] In an embodiment of the present application, after determining the coefficient matrix corresponding to each segment of magnetic data, the electronic device may further establish a magnetic compensation target function according to the coefficient matrix corresponding to each segment of magnetic data and each segment of magnetic data.

[0148] It should be noted that, in the embodiment of the present application, the magnetic compensation target function of each segment of magnetic data, the coefficient matrix corresponding to each segment of magnetic data, and each segment of magnetic data are in one-to-one correspondence.

[0149] Further, Figure 7 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 7 ,like Figure 7 As shown, the method of compensating each segment of magnetic data based on the compensation parameters of each segment of magnetic data and determining the compensated magnetic gradient data specifically includes the following steps:

[0150] Step 1041: input the compensation parameters of each segment of magnetic data and each segment of magnetic data into a full tensor magnetic gradient compensation model to determine each segment of magnetic data after compensation.

[0151] In an embodiment of the present application, after determining the compensation parameters of each segment of magnetic data in the first magnetic data, the electronic device can further input the compensation parameters of each segment of magnetic data and each segment of magnetic data into a full tensor magnetic gradient compensation model to determine each segment of magnetic data after compensation.

[0152] It should be noted that, in the embodiment of the present application, the full tensor magnetic gradient compensation model compensates each segment of magnetic data separately.

[0153] It should be noted that, in the embodiment of the present application, each segment of magnetic data can be defined according to the above formula (6).

[0154] Step 1042: splice each segment of compensated magnetic data to determine compensated magnetic gradient data.

[0155] In an embodiment of the present application, after the compensation parameters of each segment of magnetic data and each segment of magnetic data are input into the full tensor magnetic gradient compensation model to determine each segment of magnetic data after compensation, the electronic device can further splice each segment of magnetic data after compensation to determine the compensated magnetic gradient data.

[0156] It should be noted that in the embodiment of the present application, after the full tensor magnetic gradient compensation model compensates each segment of magnetic data separately, the electronic device needs to splice the compensated magnetic data of each segment to form a complete compensated magnetic gradient data. In this way, the present application performs segmented compensation on low-altitude magnetic data, which not only reduces the complex collinearity of the magnetic data, but also improves the compensation accuracy of the magnetic data.

[0157] It should be noted that, in the embodiment of the present application, the compensated magnetic gradient data can be defined according to the above formula (7).

[0158] Exemplarily, the compensation effect of the data compensation method proposed in this application is verified and explained below through actual measured data.

[0159] It should be noted that in the embodiment of the present application, a superconducting airborne full-tensor magnetic gradient measurement system is installed in a pod, which is hung under the aircraft with a cable, and the actual flight altitude of the aircraft is 200m; the survey line direction is north-south, the latitude starting point is 31.9888°, the end point is 32.0795°, and the survey line spacing is 600m; a superconducting magnetic gradient meter is used to detect the full-tensor magnetic gradient data on the survey line; the superconducting magnetic gradient meter includes 3 magnetometer sensors and 6 magnetic gradient sensors; the magnetometer data is used to compensate for the imbalance of the magnetic gradiometer. Figure 8 This is a flight path diagram of an aircraft flying in a pod as proposed in an embodiment of the present application. Fig. 9 This is the track diagram after the survey line cutting proposed in the embodiment of the present application, from Figure 8 It can be seen that the flight path of the aircraft is winding. After cutting the line, it is as follows Fig. 9 As shown, the flight path of the aircraft is a regular flight path of equal length, which is convenient for the subsequent pre-processing of the survey line data, that is, automatic removal of invalid and abnormal survey line data.

[0160] It should be noted that in the embodiment of the present application, the step length step=100, k=5, n=500 are set, and the invalid abnormal automatic removal processing is performed on the measurement line data. Fig.10 The schematic diagram of the amplitude comparison between the magnetic data before processing (full tensor magnetic gradient data) and the magnetic data after processing (first magnetic data) shows that the invalid abnormal data in the survey line data are obviously removed.

[0161] Take a channel as an example. Fig.11Schematic diagram for comparing the gradient values ​​of the original magnetic gradient data, the magnetic gradient data after traditional high-altitude compensation and the magnetic gradient data after compensation in this application, that is, a schematic diagram for comparing the gradient values ​​of the original data, the data compensated by the traditional compensation method and the data compensated by the data compensation method in this application, from Fig.11 It can be seen that the fluctuation width of the magnetic gradient data after compensation in the present application is significantly smaller than the fluctuation width of the magnetic gradient data after traditional high-altitude compensation. It can be seen that the magnetic gradient data after compensation in the present application is more accurate than the magnetic gradient data after traditional high-altitude compensation.

[0162] Table 1 Quality evaluation results of traditional high-altitude compensation method and data compensation method

[0163]

[0164] Table 1 shows the quality evaluation results corresponding to the traditional high-altitude compensation method and the data compensation method. It can be seen from Table 1 that the root mean square of the magnetic gradient data after compensation in this application is less than 10pT / m, and the improvement ratio IR is 5.68, which are better than the quality evaluation results corresponding to the traditional high-altitude compensation method. It can also be seen that the data compensation method proposed in this application can obtain good compensation results.

[0165] In addition, in order to verify the stability and consistency of the full tensor magnetic gradient compensation model and compensation parameter solution, it is necessary to compensate the repeated line data. The aircraft flies at a constant speed of 80 knots / h (1 knot = 1.852km). Affected by the wind direction, the speed and heading will deviate slightly. The two repeated lines are synchronized with the position data and repeated twice in the same direction. Among them, the repeated line flight altitude is 800m and the length is 10km.

[0166] Fig.12 FIG. 1 is a schematic diagram comparing the Bzz components of two compensated repeated line gradient data (the first repeated line gradient data and the second repeated line gradient data) proposed in the embodiment of the present application. Fig.12 It can be seen that although the aircraft heading and flight speed are affected by wind direction during the two flights, which makes the tensor components of the two repeated lines not completely overlap, the overall change trend of the Bzz component of the two repeated line magnetic gradient data after compensation can remain consistent, especially in the second half, even if there are large abnormal changes, the Bzz component of the two repeated line magnetic gradient data can also be highly consistent, which shows that the full tensor magnetic gradient compensation model and compensation parameter solution algorithm of this application have high stability and consistency.

[0167] It should be noted that, in an embodiment of the present application, an embodiment of the present application provides a data compensation method, wherein the data compensation method is applied to an electronic device, and the electronic device can first establish a full tensor magnetic gradient compensation model, and then obtain full tensor magnetic gradient data; after performing line cutting on the full tensor magnetic gradient data, remove the abnormal data therein, so as to obtain second magnetic data and inertial navigation data, and downsample the second magnetic data so that its data length is the same as the inertial navigation data length; on the premise of obtaining the first magnetic data, determine the mutation position based on the acquired yaw angle dynamic data, and determine the position to be segmented corresponding to the first magnetic data based on the mutation position, and then divide the first magnetic data into segments. After the data is segmented, each segment of magnetic data is determined; a magnetic compensation objective function is established based on the coefficient matrix corresponding to each segment of magnetic data and each segment of magnetic data, and based on the magnetic compensation objective function, compensation parameters of each segment of magnetic data are determined; the compensation parameters of each segment of magnetic data and each segment of magnetic data are input into the full tensor magnetic gradient compensation model to determine each segment of magnetic data after compensation, each segment of magnetic data after compensation is spliced ​​to determine the compensated magnetic gradient data; high-frequency noise is filtered on the compensated magnetic gradient data to determine the filtered magnetic gradient data; the compensation effect is evaluated based on the compensation improvement ratio and root mean square of the full tensor magnetic gradient data and the compensated filtered magnetic gradient data.

[0168] It can be seen that in the embodiments of the present application, the first magnetic data is obtained by preprocessing the full tensor magnetic gradient data, and the compensation parameters of each segment of magnetic data in the first magnetic data are further obtained, so that each segment of magnetic data can be compensated accordingly according to the compensation parameters of each segment of magnetic data, and the high-frequency noise in the compensated magnetic gradient data can be filtered out, thereby achieving accurate compensation of low-altitude full tensor magnetic gradient data.

[0169] Based on the above embodiments, another embodiment of the present application proposes a data compensation method, which can be applied to a data compensation device or an electronic device, and the present application does not make any specific limitation on this.

[0170] It should be noted that in the embodiments of the present application, Fig.13 A schematic diagram of a data compensation method proposed in an embodiment of the present application Figure 8 ,like Fig.13 As shown, a data compensation method proposed in an embodiment of the present application is applied to a super-navigation space full tensor magnetic gradient measurement system. Specifically, it includes the following steps:

[0171] Step 201: classify interference sources of the super-navigation space full tensor magnetic gradient measurement system.

[0172] It should be noted that in the embodiments of the present application, the interference sources are divided into internal noise of the magnetic gradient sensor and external noise of the magnetic gradient sensor. Among them, the internal noise may include the noise caused by the sensitivity difference of multiple sensors, the noise caused by the non-orthogonality of the three axes of the sensor or the installation error, and the imbalance interference; the external noise may include residual magnetism / permanent magnetism (hard magnetic interference, constant deviation), magnetic induction (soft magnetic interference, background magnetic field interference) and eddy current magnetic field (electromagnetic interference, related to the magnetic field and the rate of change of motion posture).

[0173] Step 202: Establish a full tensor magnetic gradient compensation model using a magnetic dipole equivalent source method.

[0174] It should be noted that magnetic bodies have non-uniform magnetic properties, so the traditional magnetic compensation model needs to use the tensor model κ ij (3×3) is used to represent the magnetic susceptibility. The induced magnetic field generated by the single component of the geomagnetic field is not necessarily consistent with the direction of the geomagnetic field. As the component of the maneuvering magnetic field in the measurement coordinate system changes with the movement, the magnetization intensity modulus also changes accordingly. In addition, the equivalent source method used in the traditional magnetic compensation model is based on the additional interference of field->field. It can be seen that the traditional magnetic compensation model has a large degree of freedom and a high complexity.

[0175] It should be noted that in the embodiment of the present application, the magnetization intensity modulus model is used to define the magnetic anomaly source, that is, it is assumed a priori that the magnetic susceptibility is a vector κ i (3×1), and the equivalent source method adopted in this application is based on the additional interference of source->field, that is, it is assumed that the model of the interference source is established based on a combination of one or more interference sources, which is not only easy to model multi-component fields such as tensor fields, but also helps to reduce the compensation coefficients used for modeling.

[0176] Specifically, for the internal noise of the sensor of the cryogenic ultra-high-speed navigation full-tensor magnetic gradient measurement system, sensors with similar performance are selected to build a vector magnetic gradient measurement system, the deviation introduced by the inconsistency of the sensors is weakened, and a sensor imbalance compensation model is established at the same time; for the external noise of the sensor, it is assumed that the interference source is a point-like magnetic dipole source and the position can be estimated, and the flight platform is a uniformly magnetized sphere. The external magnetic compensation model is established by integrating the classical magnetic compensation framework; the internal noise and external noise of the sensor are unified to establish a full tensor magnetic gradient compensation model. It can be seen that the degree of freedom and complexity of the full tensor magnetic gradient compensation model of this application are less than the degree of freedom and complexity of the traditional magnetic compensation model.

[0177] It should be noted that in the embodiments of the present application, for a sensor or a magnetic gradient component, the residual magnetic interference is a fixed scalar, and the three components cannot be identified in the measurement error. Therefore, the residual magnetic interference and the system deviation can be combined into a fixed constant.

[0178] It should be noted that in the embodiment of the present application, for a magnetic gradient sensor, in the pod coordinate system, the full tensor magnetic gradient comprehensive compensation model can be obtained by integrating the sensor external noise and imbalance compensation. The full tensor magnetic gradient compensation model of a single channel can be defined according to the above formula (3).

[0179] It should be noted that, in the embodiments of the present application, the coefficient matrix can be defined according to the above formula (4).

[0180] It should be noted that, in the embodiment of the present application, the compensation coefficient can be defined according to the above formula (5):

[0181] It should be noted that, in the embodiment of the present application, the full tensor magnetic gradient compensation model of a single channel takes into account the external noise and internal noise (imbalance interference) of the magnetic gradient sensor.

[0182] Step 203: preprocess the full tensor magnetic gradient data.

[0183] It should be noted that, in the embodiments of the present application, preprocessing includes line cutting, automatic removal of invalid anomalies, and downsampling.

[0184] It should be noted that, in the embodiment of the present application, survey line cutting is to cut the full tensor magnetic gradient data (flight data) into processable survey line data.

[0185] Specifically, the flight direction of the survey line is determined according to the longitude and latitude data of the inertial navigation system, the common starting point and ending point are determined based on the parallel survey lines in the same direction, and the data (full tensor magnetic gradient data) is cut according to the coordinate data of the starting point and the ending point. That is, after the full tensor magnetic gradient data is subjected to survey line cutting processing, the cut full tensor magnetic gradient data, that is, the survey line data, is obtained.

[0186] It should be noted that in the embodiment of the present application, the automatic removal of invalid abnormalities is aimed at the characteristic that superconducting chips are prone to losing lock, and the invalid abnormal data in the measurement line data is removed to obtain the second magnetic data and inertial navigation data. The electronic device can synchronously remove the abnormal data in all channels of the superconducting full tensor magnetic measurement system based on the sliding window moving variance method to maintain the synchronization of multi-channel data.

[0187] Specifically, if there are invalid abnormal data in the survey line data, magnetic compensation processing cannot be performed directly. Among them, invalid abnormal data is the data when the superconducting chip goes from unlocking to resetting, which is considered to be unreliable and needs to be removed by synchronous inertial navigation data. In addition, affected by high-altitude airflow, the pod in the superconducting full tensor magnetic measurement system may produce violent attitude vibrations, resulting in large fluctuations in the survey line data. At this time, the collected signal amplitude will fluctuate violently within the abnormal range. Therefore, invalid abnormal data in the survey line data can also be data with violent fluctuations in the signal amplitude within the abnormal range. Before magnetic compensation, both types of invalid abnormal data need to be removed synchronously.

[0188] Specifically, taking one channel as an example, the sliding window moving variance method is as follows: Assume that the original full tensor magnetic gradient data of a survey line is {P1,…,P N-1 , P N}, where N is the total number of data points on the survey line; calculate the moving variance value of all data on the survey line at a certain step length (step) to obtain the variance sequence {Q1, Q2…, Qm}; then calculate the average value of the variance sequence; finally remove the n data before and after the value greater than k times the average value in the variance sequence. Where k and n can be set according to actual conditions.

[0189] It should be noted that, in the embodiment of the present application, downsampling is to process the second magnetic data and the inertial navigation data into data sets of equal length.

[0190] Specifically, the sampling rates of the second magnetic data and the inertial navigation data are different. Before magnetic compensation, the second magnetic data needs to be downsampled to obtain the first magnetic data and inertial navigation data of equal length. The present application adopts the mean sampling method to downsample the second magnetic data, which can well retain the detailed information in the magnetic signal.

[0191] Step 204: construct a magnetic compensation objective function and solve the compensation parameters.

[0192] It should be noted that in the embodiments of the present application, each coefficient in the full tensor magnetic gradient compensation model has very serious complex collinearity. If the traditional L2 norm data fitting method is used to solve the compensation coefficient, due to its unbiased estimation characteristics, when the residual between the calculated value and the measured value has a significant amplitude change, the method will perform indifferent fitting on the effective signal, and ultimately cause the compensated magnetic gradient data to have obvious invalid signal oscillations near the effective signal area, thereby reducing the compensation accuracy of the signal. In order to ensure the stability of the solution, the present application uses the ridge regression algorithm to solve the compensation coefficient. The ridge regression algorithm can solve the complex collinearity problem of the full tensor magnetic gradient compensation model by adding constraints to the coefficients.

[0193] It should be noted that in the embodiment of the present application, when the yaw angle changes suddenly during the flight, the gradiometer magnetic data (first magnetic data) will also show obvious abnormal changes, such as Fig.14 The schematic diagram of the amplitude comparison between the first magnetic data and the yaw angle dynamic data shown is a schematic diagram of the amplitude comparison between the yaw angle data and the gradiometer data. Therefore, the present application proposes a method of dividing the first magnetic data into several segments according to the position where the yaw angle changes suddenly and compensating them separately.

[0194] It should be noted that in the embodiment of the present application, the position where the yaw angle suddenly changes is first found, and the gradiometer magnetic data (first magnetic data) is divided into j segments according to the position where the yaw angle suddenly changes; then, for each segment of magnetic gradient data, an objective function (magnetic compensation objective function) is established; finally, the compensation coefficient of each segment of magnetic data is solved based on the objective function. The present application uses the ridge regression algorithm to solve the compensation parameters of each segment of magnetic data, and then calculates the compensated magnetic gradient data, and finally splices all the compensated data together.

[0195] It should be noted that, in the embodiment of the present application, the magnetic compensation target function of each segment of magnetic data can be defined according to the above formula (1).

[0196] It should be noted that, in the embodiment of the present application, the compensation parameter of each segment of magnetic data can be defined according to the above formula (2).

[0197] It should be noted that, in the embodiment of the present application, each segment of magnetic data can be defined according to the above formula (6).

[0198] It should be noted that, in the embodiment of the present application, the compensated magnetic gradient data can be defined according to the above formula (7).

[0199] Step 205: high frequency noise processing.

[0200] It should be noted that, in the embodiment of the present application, the high-frequency noise processing refers to filtering the high-frequency noise of the compensated magnetic gradient data to obtain filtered magnetic gradient data.

[0201] It should be noted that, in the embodiment of the present application, the compensated magnetic gradient data still contains some high-frequency noise that cannot be filtered out, so it is necessary to further filter the compensated magnetic gradient data according to the signal frequency band characteristics of the actual application problem.

[0202] It should be noted that, in the embodiment of the present application, the electronic device may use low-pass filtering to filter out high-frequency noise in the compensated magnetic gradient data.

[0203] It should be noted that, in the embodiments of the present application, the interference in the compensated magnetic gradient data mainly comes from the magnetic anomalies caused by the lithosphere.

[0204] Step 206: Evaluate compensation effect.

[0205] It should be noted that, in the embodiments of the present application, the compensation effect evaluation is characterized by evaluating the compensation effect based on the compensation improvement ratio and the root mean square of the full tensor magnetic gradient data and the filtered magnetic gradient data.

[0206] It should be noted that, in the embodiments of the present application, the compensation improvement ratio represents the ratio of the standard deviation of the full tensor magnetic gradient data to the filtered magnetic gradient data. Generally, the larger the compensation improvement ratio, the better the compensation effect.

[0207] It should be noted that, in the embodiment of the present application, the compensation improvement ratio can be defined according to the above formula (8).

[0208] It should be noted that in the embodiments of the present application, the root mean square represents the effective value of the data. Generally, the smaller the effective value of the filtered magnetic gradient data is, the better the compensation effect is.

[0209] It should be noted that, in the embodiments of the present application, the root mean square can be defined according to the above formula (9).

[0210] The embodiment of the present application provides a data compensation method, wherein the data compensation method is applied to an electronic device, and the electronic device can first establish a full tensor magnetic gradient compensation model; then obtain full tensor magnetic gradient data, and pre-process the full tensor magnetic gradient data to determine the first magnetic data; and determine the compensation parameters of each segment of magnetic data in the first magnetic data; then compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine the compensated magnetic gradient data; and filter the compensated magnetic gradient data for high-frequency noise to determine the filtered magnetic gradient data. It can be seen that in the embodiment of the present application, the first magnetic data is obtained by pre-processing the full tensor magnetic gradient data, and the compensation parameters of each segment of magnetic data in the first magnetic data are further obtained, so that each segment of magnetic data can be compensated according to the compensation parameters of each segment of magnetic data, and the high-frequency noise in the compensated magnetic gradient data can be filtered out, so that accurate compensation of low-altitude full tensor magnetic gradient data is achieved.

[0211] Based on the same inventive concept as the above embodiment, another embodiment of the present application provides a data compensation device, Fig.15 FIG. 1 is a schematic diagram of the structure of a data compensation device. Fig.15 As shown, the data compensation device 30 includes: an acquisition unit 301 , a processing unit 302 , a determination unit 303 and a compensation unit 304 .

[0212] An acquisition unit 301 is used to acquire full tensor magnetic gradient data;

[0213] A processing unit 302 is used to pre-process the full tensor magnetic gradient data to determine first magnetic data;

[0214] A determination unit 303, configured to determine a compensation parameter for each segment of magnetic data in the first magnetic data;

[0215] The compensation unit 304 is used to compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine compensated magnetic gradient data.

[0216] The present application also provides a schematic diagram of the structure of an electronic device. Fig.16 As shown, the electronic device 40 proposed in the embodiment of the present application may include a processor 41 and a memory 42 configured to store a computer program that can be run on the processor. Furthermore, the electronic device 40 may also include a communication interface 43 and a bus 44 for connecting the processor 41, the memory 42 and the communication interface 43.

[0217] In the embodiment of the present application, the processor 41 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor function may also be other, and the embodiment of the present application does not specifically limit it.

[0218] In the embodiment of the present application, the memory 42 is used to store instructions and data. The memory 42 may be connected to the processor 41, wherein the memory 42 is used to store executable program codes, which include computer operation instructions.

[0219] In the embodiment of the present application, the bus 44 is used to connect the communication interface 43, the processor 41 and the memory 42, and the mutual communication between these devices.

[0220] Furthermore, in an embodiment of the present application, the processor 41 is used to obtain full tensor magnetic gradient data; after preprocessing the full tensor magnetic gradient data, determine the first magnetic data; and further determine the compensation parameters of each segment of magnetic data in the first magnetic data; and then compensate each segment of magnetic data based on the compensation parameters of each segment of magnetic data to determine the compensated magnetic gradient data.

[0221] In practical applications, the memory 42 may be a volatile memory, such as a random access memory (RAM); or a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 41.

[0222] The embodiment of the present application provides a data compensation device and an electronic device, wherein the data compensation device or the electronic device first obtains full tensor magnetic gradient data; then, preprocesses the full tensor magnetic gradient data to determine the first magnetic data; and determines the compensation parameters of each segment of magnetic data in the first magnetic data; finally, based on the compensation parameters of each segment of magnetic data, each segment of magnetic data is compensated to determine the compensated magnetic gradient data. It can be seen that in the embodiment of the present application, the first magnetic data is obtained by preprocessing the full tensor magnetic gradient data, and the compensation parameters of each segment of magnetic data in the first magnetic data are further obtained, so that each segment of magnetic data can be compensated according to the compensation parameters of each segment of magnetic data to obtain the compensated magnetic data, thereby realizing accurate compensation of the low-altitude full tensor magnetic gradient data.

[0223] The embodiment of the present application also provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the data compensation method as described above is implemented.

[0224] Specifically, the program instructions corresponding to a data compensation method in this embodiment can be stored in a storage medium such as a CD, a hard disk, a USB flash drive, etc. When the program instructions corresponding to a data compensation method in the storage medium are read or executed by an electronic device, the following steps are included:

[0225] Acquire full tensor magnetic gradient data;

[0226] Preprocessing the full tensor magnetic gradient data to determine first magnetic data;

[0227] determining a compensation parameter for each segment of magnetic data in the first magnetic data;

[0228] Each segment of magnetic data is compensated based on the compensation parameter of each segment of magnetic data to determine compensated magnetic gradient data.

[0229] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of hardware embodiments, software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0230] The present application is described with reference to implementation flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0231] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which is implemented in the implementation flow diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0232] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing the steps in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0233] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A data compensation method, characterized in that: The method comprises: Acquire full tensor magnetic gradient data; Preprocessing the full tensor magnetic gradient data to determine first magnetic data and yaw angle dynamic data of the pod at the same time; wherein the first magnetic data is magnetic gradient data obtained after performing line cutting processing, invalid abnormal automatic removal processing and downsampling processing on the full tensor magnetic gradient data in sequence; the pod is hung under the aircraft with a cable; Segmenting the first magnetic data based on the yaw angle dynamic data of the pod to determine each segment of magnetic data in the first magnetic data; Establishing a magnetic compensation target function for each segment of magnetic data, and determining a compensation parameter for each segment of magnetic data based on the magnetic compensation target function; Each segment of magnetic data is compensated based on the compensation parameters of each segment of magnetic data to determine the compensated magnetic gradient data corresponding to the full tensor magnetic gradient data; wherein, Preprocessing the full tensor magnetic gradient data to determine first magnetic data includes: Performing line cutting on the full tensor magnetic gradient data to obtain line data; Removing abnormal data from the survey line data to obtain second magnetic data and inertial navigation data; Downsampling the second magnetic data to obtain the first magnetic data having the same length as the inertial navigation data; The step of segmenting the first magnetic data based on the yaw angle dynamic data of the pod to determine each segment of magnetic data in the first magnetic data includes: Determine a mutation position corresponding to the first magnetic data according to the yaw angle dynamic data of the pod; Based on the mutation position, determining a to-be-segmented position corresponding to the first magnetic data; Segmenting the first magnetic data based on the to-be-segmented positions to determine each segment of magnetic data; The removing of abnormal data in the survey line data comprises: The sliding window moving variance method is used to automatically remove invalid anomalies from the measurement line data; wherein the abnormal data represents the data of the superconducting chip in the superconducting full tensor magnetic measurement system from loss of lock to reset.

2. The method according to claim 1, characterized in that Before acquiring the full tensor magnetic gradient data, the method further includes: Acquire internal noise data, and construct a first magnetic compensation model based on the internal noise data; wherein the internal noise data is used to determine the internal noise of the sensor; Acquire external noise data, and construct a second magnetic compensation model based on the external noise data; wherein the external noise data is used to determine the external noise of the sensor; A full tensor magnetic gradient compensation model is established based on the first magnetic compensation model and the second magnetic compensation model.

3. The method according to claim 2, characterized in that The compensating each segment of magnetic data based on the compensation parameter of each segment of magnetic data to determine the compensated magnetic gradient data corresponding to the full tensor magnetic gradient data includes: Inputting the compensation parameters of each segment of magnetic data and each segment of magnetic data into the full tensor magnetic gradient compensation model to determine each segment of magnetic data after compensation; Each segment of the compensated magnetic data is spliced ​​to determine the compensated magnetic gradient data.

4. The method according to any one of claims 1 to 3, characterized in that The step of establishing the magnetic compensation target function for each segment of magnetic data comprises: Determine a coefficient matrix corresponding to each segment of magnetic data; The magnetic compensation target function is established according to the coefficient matrix corresponding to each segment of magnetic data and each segment of magnetic data.

5. The method according to any one of claims 1 to 3, characterized in that After compensating each segment of magnetic data based on the compensation parameters of each segment of magnetic data and determining the compensated magnetic gradient data corresponding to the full tensor magnetic gradient data, the method further includes: The compensated magnetic gradient data is subjected to high frequency noise filtering to obtain filtered magnetic gradient data.

6. A data compensation device, characterized in that: The device comprises: An acquisition unit, used for acquiring full tensor magnetic gradient data; A processing unit is used to pre-process the full tensor magnetic gradient data to determine the first magnetic data and the yaw angle dynamic data of the pod at the same time; wherein the first magnetic data is the magnetic gradient data obtained after the full tensor magnetic gradient data is sequentially processed by line cutting, invalid abnormal automatic removal and downsampling; the pod is hung under the aircraft by a cable; a determining unit, configured to determine a compensation parameter for each segment of magnetic data in the first magnetic data; A compensation unit, configured to compensate each segment of magnetic data based on a compensation parameter of each segment of magnetic data, and determine compensated magnetic gradient data corresponding to the full tensor magnetic gradient data; The determination unit determines a mutation position corresponding to the first magnetic data according to the yaw angle dynamic data of the pod; determines a to-be-segmented position corresponding to the first magnetic data based on the mutation position; segments the first magnetic data based on the to-be-segmented position to determine each segment of magnetic data; establishes a magnetic compensation target function for each segment of magnetic data, and determines a compensation parameter for each segment of magnetic data based on the magnetic compensation target function; The processing unit is further used to perform line cutting on the full tensor magnetic gradient data to obtain line data; use a sliding window moving variance method to automatically remove invalid anomalies on the line data to obtain second magnetic data and inertial navigation data, wherein the abnormal data represents data from the entire process from loss of lock to reset of the superconducting chip in the superconducting full tensor magnetic measurement system; and perform downsampling on the second magnetic data to obtain the first magnetic data with the same length as the inertial navigation data.

7. An electronic device, characterized in that: The electronic device comprises: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the compensation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the compensation method according to any one of claims 1 to 5 is implemented.

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