Data compensation method, device, apparatus, and storage medium

Abstract

The embodiment of the application discloses a data compensation method, device, equipment and storage medium, and the method comprises the steps of establishing a target interference compensation model of a towed superconducting full-tensor magnetic gradient measurement system for a pod platform, wherein the target interference compensation model comprises a SQUID magnetic gradiometer measurement item, an interference compensation item and a sensor self-interference correction item; wherein the interference compensation item excludes residual magnetic interference items of the pod platform, and the interference compensation item comprises a magnetosensitive interference item represented by a first-order derivative of an earth magnetic field component and an eddy current interference item represented by a second-order derivative of the earth magnetic field component; the sensor self-interference correction item is used for representing the imbalance degree and unknown offset of the SQUID magnetic gradiometer; a target function is constructed based on the target interference compensation model; the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value are input into the target function, and a compensation coefficient is obtained by using a symmetric overrelaxation iteration method; and the towed superconducting full-tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

Description

TECHNICAL FIELD

[0001] The present application relates to, but is not limited to, the technical field of airborne magnetic exploration, and particularly relates to a data compensation method and device, equipment and a storage medium. BACKGROUND

[0002] Superconducting quantum interference device (SQUID) has become the core technology in the field of airborne magnetic surveying due to its high sensitivity of fT level. The superconducting airborne full tensor magnetic gradient measurement system constructed by multiple SQUIDs can realize high-precision measurement of all elements of the geomagnetic field, and its measurement precision is improved by 1-2 orders of magnitude compared with the traditional airborne magnetic technology, which provides a revolutionary means for deep resource exploration.

[0003] However, the dynamic magnetic interference of the aircraft seriously restricts the performance of the system, and the data without effective compensation will produce significant deviation, affecting the analysis of the magnetic field source characteristics. SUMMARY

[0004] Therefore, the embodiments of the present application provide at least a data compensation method, device, equipment and storage medium.

[0005] The technical scheme of the embodiments of the present application is as follows:

[0006] On the one hand, the embodiments of the present application provide a data compensation method, which comprises:

[0007] A target interference compensation model of the towed superconducting full tensor magnetic gradient measurement system is established based on the pod platform, and the target interference compensation model at least comprises a SQUID magnetic gradiometer measurement item, an interference compensation item and a sensor self-interference correction item; wherein the interference compensation item excludes the residual magnetic interference item of the pod platform, and the interference compensation item comprises a magnetic sensing interference item represented by the first order derivative of the geomagnetic field component and an eddy current interference item represented by the second order derivative item of the geomagnetic field component; the sensor self-interference correction item is used to represent the imbalance degree and unknown offset of the SQUID magnetic gradiometer itself;

[0008] A target function is constructed based on the target interference compensation model;

[0009] The SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value are input into the target function, and a compensation coefficient is obtained by using a symmetric overrelaxation iteration method;

[0010] The towed superconducting full tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

[0011] On the other hand, the embodiments of the present application provide a data compensation device, which comprises:

[0012] The construction module is configured to establish a target interference compensation model of the towed superconducting full-tensor magnetic gradient measurement system, and the target interference compensation model at least includes a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term; the interference compensation term excludes a residual magnetic interference term of the pod platform, and the interference compensation term includes a magnetosensitive interference term represented by a first-order derivative of an earth magnetic field component and an eddy current interference term represented by a second-order derivative term of the earth magnetic field component; the sensor self-interference correction term is configured to represent an imbalance degree and an unknown offset of the SQUID magnetic gradiometer; a target function is constructed based on the target interference compensation model, and input parameters of the target function include a SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value.

[0013] The compensation module is configured to input the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value into the target function, solve the compensation coefficient by using a symmetric overrelaxation iteration method, and compensate the towed superconducting full-tensor magnetic gradient measurement system based on the compensation coefficient.

[0014] In another aspect, an embodiment of the present application provides a computer device, including a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements part or all steps of the above data compensation method when executing the program.

[0015] In another aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement part or all steps of the above data compensation method.

[0016] In another aspect, an embodiment of the present application provides a computer program, which includes computer readable code, and when the computer readable code runs in a computer device, a processor in the computer device executes part or all steps for implementing the above data compensation method.

[0017] In another aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer readable storage medium storing a computer program, and when the computer program is read and executed by a computer, part or all steps of the above data compensation method are implemented.

[0018] In the embodiment of the present application, the interference compensation model is established for the towed superconducting full tensor magnetic gradient measurement system, the interference compensation model excludes the residual magnetic interference term of the pod platform, introduces the magnetosensitive interference term represented by the first order derivative of the earth magnetic field component and the eddy current interference term represented by the second order derivative term of the earth magnetic field component, accurately describes the interference mechanism specific to the SQUID magnetic gradiometer, adapts to the soft connection characteristics of the towed system, realizes high-precision compensation of the superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but not limiting the technical solutions of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0021] Figure 1 A schematic diagram of a SQUID magnetic gradiometer provided for the embodiment of the present application;

[0022] Figure 2 An implementation flowchart of a data compensation method provided for the embodiment of the present application;

[0023] Figure 3 A coordinate system schematic diagram with the center of the bottom surface of the hexagonal platform as the origin provided for the embodiment of the present application;

[0024] Figure 4 A configuration schematic diagram of a pod system provided for the embodiment of the present application;

[0025] Figure 5 A flight test track diagram provided for the embodiment of the present application;

[0026] Figure 6 A compensation result comparison diagram provided for the embodiment of the present application;

[0027] Figure 7 A component structure schematic diagram of a data compensation device provided for the embodiment of the present application;

[0028] Figure 8 A hardware entity schematic diagram of a computer device provided for the embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application are further described in detail below in combination with the drawings and embodiments, and the described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0030] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0031] The terms "first / second / third" referred to are only to distinguish similar objects, and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0032] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as commonly understood by those skilled in the art to which the present application belongs. The terms used in the present application are only for the purpose of describing the present application and are not intended to limit the present application.

[0033] Superconducting quantum interference device (SQUID) has become the core technology in the field of airborne magnetic surveying due to its high sensitivity of fT level. The superconducting airborne full-tensor magnetic gradient measurement system constructed by multiple SQUIDs can realize high-precision measurement of all elements of the geomagnetic field

[0034] The measurement accuracy is improved by 1-2 orders of magnitude compared with traditional airborne magnetic technology, providing a revolutionary means for deep resource exploration and military detection. However, the dynamic magnetic interference of the aircraft seriously restricts the performance of the system, and the data without effective compensation will produce significant deviation, affecting the analysis of the characteristics of the magnetic field source.

[0035] Traditional airborne magnetic compensation technology mainly uses the Tolles-Lawson model to describe the interference of the residual magnetic field, induced field and eddy current field of the aircraft through a linear equation system, and needs to obtain compensation coefficients through specific maneuvering flight. Typical implementation methods include:

[0036] Airborne compensation: the magnetic gradiometer is installed close to the fuselage, and the parameters are solved through "8" shaped maneuvering flight, but the system is complex and the compensation accuracy is limited;

[0037] Ground-based calibration: the parameters are obtained by measuring the magnetic field around the fuselage on the ground, but the position of the pod changes dynamically in actual flight, resulting in significant compensation error;

[0038] Cross modulation method: three-axis compensation is achieved by alternating the application of modulation magnetic field, but the operation is complex and the response speed is slow.

[0039] These methods have the following fundamental defects: 1) connection mode limitation: the towed system uses soft connection, which cannot perform complex maneuvering actions required by traditional compensation; 2) principle mismatch: the SQUID magnetic gradiometer reflects the dynamic change of the magnetic field by detecting the difference between the magnetic flux changes in the two superconducting rings, rather than measuring the background magnetic field, such as Figure 1 As shown in the figure, the difference in measurement principle makes the traditional magnetic compensation model unable to accurately describe its interference characteristics; 3) insufficient signal processing: superconducting magnetic gradient data belongs to the category of weak signal measurement, and the interference signal and the effective signal are of the same order of magnitude, so it is difficult for traditional methods to effectively separate them.

[0040] The traditional Tolles-Lawson compensation scheme requires the aircraft to perform ±10° multi-axis maneuvering flight (pitch / yaw / roll), while the towed system cannot complete such complex actions due to the soft connection characteristics, and the SQUID is prone to lose lock (loss rate of working point > 30%) in complex maneuvering flight, resulting in interruption in data acquisition; the traditional magnetic compensation model (residual magnetism / susceptibility / eddy current) fails to closely combine the quantum measurement principle of the SQUID magnetic sensor, and the analysis of the sources of system interference is insufficient, resulting in a serious disconnection between the compensation model and the actual working conditions, and the inability to accurately characterize the interference mechanism in complex environments; the traditional least squares method parameter solving method has deteriorated signal-to-noise ratio under weak signal conditions, and produces invalid fluctuations in non-magnetic source areas.

[0041] To solve the above problems, the present application proposes a special magnetic compensation method suitable for the towed superconducting airborne magnetic survey system, establishes a special compensation model based on the measurement principle of the SQUID magnetic gradiometer, accurately describes the interference mechanism of the magnetic gradiometer, develops a compensation coefficient solving algorithm without complex maneuvering, adapts to the constraints of the towed system, and designs an extremely weak signal separation technology to improve the interference extraction accuracy. Through systematic model building, parameter solving and effect evaluation, a complete superconducting magnetic gradient data compensation technology system is formed, breaking through the limitations of existing methods in precision and applicability. The implementation of the technology is expected to significantly improve the overall accuracy of superconducting magnetic survey data, provide an efficient and reliable compensation solution for superconducting airborne magnetic survey systems, and promote China to achieve a technological breakthrough in this field and narrow the gap with international advanced levels.

[0042] The data compensation method provided by the embodiments of the present application can be executed by a processor of a computer device. The computer device can be a server, a notebook computer, a tablet computer, a desktop computer, a smart television, a set-top box, a mobile device (such as a mobile phone, a portable video player, a personal digital assistant, a dedicated messaging device, a portable game device), or the like, which has a towed superconducting airborne full-tensor magnetic gradiometer data compensation capability. Figure 2An implementation flowchart of a data compensation method provided by an embodiment of the present application is shown in FIG. 1. Figure 2 The method includes the following steps.

[0043] In step 101, a target interference compensation model of the towed superconducting full-tensor magnetic gradient measurement system is established, and the target interference compensation model includes at least a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes a residual magnetic interference term of the gondola platform, and the interference compensation term includes a magnetic induction interference term represented by a first-order derivative of an earth magnetic field component and an eddy current interference term represented by a second-order derivative term of the earth magnetic field component. The sensor self-interference correction term is used to represent an unbalance degree and an unknown offset of the SQUID magnetic gradiometer. The SQUID magnetic gradiometer reflects the dynamic change of the magnetic field by measuring the difference between the magnetic flux changes in the superconducting rings, and is not sensitive to the residual magnetism that remains stable for a short time, but is extremely sensitive to the high-order derivative terms of the magnetic field. Therefore, the interference compensation model introduces the high-order derivative terms to accurately reflect the sensitivity of the SQUID to the dynamic magnetic field, and weakens the stable interference terms such as residual magnetism. That is, the interference model excludes the residual magnetic interference term of the gondola platform, and the interference compensation term includes the magnetic induction interference term represented by the first-order derivative of the earth magnetic field component, the eddy current interference term represented by the second-order derivative term of the earth magnetic field component, and the unbalance degree and unknown offset interference term from the SQUID magnetic sensor itself.

[0044] In the embodiment of the present application, the gondola platform refers to a device suspended below an aircraft for carrying a superconducting full-tensor magnetic gradient measurement system. The SQUID magnetic gradiometer is a magnetic flux-voltage converter based on the superconducting quantum interference effect, which can measure the difference between the magnetic flux changes in two superconducting rings. The target interference compensation model is a mathematical model used to describe and eliminate various interference factors in the measurement system. The magnetic induction interference term refers to the interference component generated by the gondola platform being magnetized in the earth's magnetic field. The eddy current interference term refers to the interference component caused by the eddy current induced by the gondola platform when the earth's magnetic field changes. The SQUID magnetometer is a superconducting quantum interference device used to measure the strength of the ambient magnetic field.

[0045] The system first establishes a target interference compensation model for the towed superconducting full-tensor magnetic gradient measurement system. The model includes three main components: a SQUID magnetic gradiometer measurement term for representing the measurement value of the magnetic gradiometer; an interference compensation term for modeling the interference generated by the pod platform, wherein the residual magnetic interference term is excluded because the superconducting ring is not sensitive to the constant residual magnetism due to the flux quantization characteristic of the superconducting ring; and a sensor self-interference correction term for correcting the pseudo-gradient value caused by the asymmetry of the superconducting ring by using the measurement value of the SQUID magnetometer. The system particularly considers the relationship between the magnetosensitive interference term and the first-order derivative of the earth magnetic field component and the relationship between the eddy current interference term and the second-order derivative of the earth magnetic field component in the modeling process, which is essentially different from the interference model of a traditional magnetometer.

[0046] In step 102, a target function is constructed based on the target interference compensation model.

[0047] In the embodiment of the present application, the target function is a mathematical expression for optimizing the compensation coefficient solving process, which quantifies the difference between the measurement value and the model prediction value as an index that can be optimized. The symmetric overrelaxation iteration method is an iterative algorithm for solving linear equations, which accelerates convergence by introducing a relaxation factor.

[0048] The target function includes three main parts: a first-order derivative difference term of the measurement signal and the model prediction value, which is used to amplify the high-frequency components of the interference signal; an adaptive weight coefficient, which is used to dynamically adjust the contribution of different terms according to the local magnetic field characteristics; and a variable norm order, which is used to enhance the robustness of the model to outliers. The system particularly considers the characteristics of the SQUID magnetic gradient data belonging to a weak signal measurement system when constructing the target function, and enhances the difference between the interference signal and the target signal by using a time domain differential operator to improve the signal separation effect.

[0049] In step 103, the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value are input into the target function, and the symmetric overrelaxation iteration method is used to solve the compensation coefficient.

[0050] In the embodiment of the present application, the compensation coefficient refers to the model coefficient for correcting the interference effect in the measurement data. The SQUID magnetic gradiometer measurement value refers to the magnetic flux change difference signal actually output by the magnetic gradiometer. The SQUID magnetometer measurement value refers to the three-component value of the ambient magnetic field measured by the magnetometer.

[0051] The system inputs the actual measured SQUID magnetic gradiometer measurement and SQUID magnetometer measurement into the constructed objective function. The system solves the compensation coefficient by using a symmetric over-relaxation iteration method, which is realized by the following steps: first, initializing the coefficient matrix and the initial value of the compensation coefficient; then setting the relaxation factor, the maximum number of iterations and the stopping error threshold; then performing iterative calculation, and updating the compensation coefficient according to the current parameter value in each iteration; finally, judging whether the iteration meets the stopping condition, if yes, outputting the final compensation coefficient, otherwise, continuing the iteration until the maximum number of iterations is reached. This method ensures the stability of the solution under weak signal conditions.

[0052] In step 104, the towed superconducting full tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

[0053] In the embodiments of the present application, the system compensates the towed superconducting full tensor magnetic gradient measurement system using the compensation coefficient obtained in step 103. The compensation process includes: calculating the interference value of each segment of data according to the compensation coefficient; obtaining the compensated data by subtracting the interference value from the original measured magnetic flux; and splicing each segment of the compensated data to obtain the complete compensated magnetic gradient data. In the compensation process, the system uses different fitting orders and weight coefficients according to the magnetic source region division result, increases the proportion of signal derivative information in the low error area, and increases the proportion of data fitting degree in the high error area, so as to achieve the optimal compensation effect.

[0054] The system of the embodiments of the present application establishes an interference compensation model specially for the towed superconducting full tensor magnetic gradient measurement system. The interference compensation model accurately describes the interference mechanism specific to the SQUID magnetic gradiometer by excluding the residual magnetic interference term of the pod platform, introducing the magnetosensitive interference term represented by the first-order derivative of the earth's magnetic field component, and introducing the eddy current interference term represented by the second-order derivative term of the earth's magnetic field component, adapts to the soft connection characteristics of the towed system, realizes high-precision compensation of the superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0055] In some embodiments, the step 101 includes:

[0056] In step 1011, a traditional interference compensation model of the towed superconducting full tensor magnetic gradient measurement system caused by the pod platform is obtained, and the traditional interference compensation model at least includes a residual magnetic interference coefficient, a magnetosensitive interference coefficient and an eddy current interference coefficient.

[0057] In the embodiments of the present application, the residual magnetic interference coefficient is a parameter describing the constant magnetic field interference generated by the permanent magnetic material in the measurement system. The magnetosensitive interference coefficient is a parameter representing the interference generated by the magnetization of the measurement system in the geomagnetic field. The eddy current interference coefficient is a parameter reflecting the eddy current interference generated by the conductive material in the measurement system when the geomagnetic field changes.

[0058] The system first acquires a traditional interference compensation model of the towed superconducting full-tensor magnetic gradient measurement system for the gondola platform. The traditional interference compensation model includes three main interference parameters: residual magnetic interference coefficient, magnetosensitive interference coefficient, and eddy current interference coefficient. The system establishes a mathematical expression of the traditional interference compensation model by analyzing the structural characteristics and material composition of the gondola platform. The traditional interference compensation model is constructed based on the traditional magnetic compensation theory, providing a basic framework for subsequent model optimization. The system regards the gondola platform as a disturbance source and quantifies its influence on the superconducting magnetic gradient measurement system.

[0059] Step 1012, excluding the residual magnetic interference coefficient from the traditional interference compensation model, and deriving the magnetosensitive interference coefficient as a magnetosensitive interference term characterized by the first-order derivative of the earth's magnetic field component, and deriving the eddy current interference coefficient as an eddy current interference term characterized by the second-order derivative term of the earth's magnetic field component.

[0060] In the embodiments of the present application, the first-order derivative of the earth's magnetic field component refers to the rate of change of the earth's magnetic field in three orthogonal directions, reflecting the change characteristics of the magnetic field over time or space. The second-order derivative term of the earth's magnetic field component refers to the rate of change of the rate of change of the earth's magnetic field, characterizing the acceleration characteristics of the magnetic field change.

[0061] The system performs two key optimizations on the traditional interference compensation model: first, according to the quantum measurement characteristics of the superconducting quantum interference device (SQUID), the system excludes the residual magnetic interference coefficient, because the flux quantization characteristics of the superconducting ring make it insensitive to constant residual magnetism. Second, the system derives the magnetosensitive interference term as the product of the traditional magnetosensitive interference coefficient and the first-order derivative of the earth's magnetic field component, and derives the eddy current interference term as the product of the eddy current interference coefficient and the second-order derivative term of the earth's magnetic field component. This method accurately reflects the essential characteristics that the SQUID gradiometer measures the amount of magnetic field change rather than the magnetic field strength. The system optimizes the interference model to better conform to the actual working principle of the superconducting magnetic gradiometer.

[0062] Step 1013, introducing a sensor self-interference correction term characterized by the SQUID magnetometer measurement value into the traditional interference compensation model to obtain a target interference compensation model.

[0063] In the embodiment of the present application, the system introduces a sensor self-interference correction term in the optimized interference model, which is constructed based on the real-time measurement value of the SQUID magnetometer. The system monitors the environmental magnetic field changes through the three-axis SQUID magnetometer, establishes a correction matrix to eliminate the pseudo-gradient signal caused by the asymmetry of the superconducting ring and the installation deviation. The system mathematically integrates the correction term with the optimized interference model to form a complete target interference compensation model. This model can accurately describe the interference mechanism of the towed superconducting full-tensor magnetic gradient measurement system under actual working conditions, providing a theoretical basis for subsequent compensation coefficient solving and data processing.

[0064] The embodiment of the present application establishes an interference compensation model specially for superconducting magnetic gradient measurement system through three key steps of obtaining traditional interference compensation model, optimizing model structure and introducing sensor self-interference correction term, breaks through the limitations of traditional magnetic compensation technology, accurately reflects the special working principle of superconducting quantum interference device, and solves the technical problem that the towed system cannot perform complex maneuvering flight.

[0065] In some embodiments, the step 102 comprises:

[0066] Step 1021, obtaining the fitting term and the derivative term of the target interference compensation model.

[0067] In the embodiment of the present application, the derivative term refers to a mathematical expression obtained by performing a first-order time derivative operation on the fitting term, which is used to enhance the difference between the interference signal and the target signal. The fitting term is to make the prediction or estimation result of the model as close to the real data as possible. It is one of the most basic components in the optimization objective function.

[0068] The system first extracts the fitting term and the derivative term from the established superconducting magnetic gradient compensation model. The derivative term is obtained by calculating the first-order time derivative of the measurement signal, which can amplify the high-frequency components of the interference signal and suppress the high-frequency noise in the target signal. The fitting term is extracted as a constraint condition to maintain the stability of the model in the subsequent parameter solving process.

[0069] Step 1022, determining the adaptive weight coefficient and the variable norm order corresponding to the fitting term and the derivative term respectively.

[0070] In the embodiment of the present application, the adaptive weight coefficient refers to a weight parameter that can be dynamically adjusted according to the local magnetic field characteristics, which is used to balance the contributions of different terms in the objective function. The variable norm order refers to the norm calculation order that can be adjusted according to the signal characteristics, which enhances the robustness of the model to outliers.

[0071] The system dynamically determines adaptive weight coefficients a and β corresponding to the fitting term and the derivative term respectively according to the magnetic field characteristics of the current measurement region. These coefficients are automatically adjusted by analyzing the local characteristics of the signal, and a is increased in the high-error area to improve the data fitting degree, and β is increased in the low-error area to retain the signal fluctuation characteristics. Meanwhile, the system determines appropriate variable norm orders p and q for each term, which can be adjusted within the range of 1-2 according to the signal characteristics to enhance the adaptability of the model to different types of interference.

[0072] In step 1023, the fitting term and the derivative term are combined with the corresponding adaptive weight coefficients and variable norm orders to obtain the objective function.

[0073] In the embodiments of the present application, the system mathematically combines the obtained fitting term and derivative term with the determined adaptive weight coefficients and variable norm orders to construct a complete objective function. The derivative term DGm-DAx in the objective function is used to capture the high-frequency characteristics and boundary features of the signal, and the fitting term is used to maintain the stability of the solution. Through this combination, the system realizes accurate separation of the weak magnetic signal and provides an optimized mathematical framework for subsequent parameter solving.

[0074] The embodiments of the present application can effectively handle the weak signal separation problem in superconducting magnetic gradient measurement by systematically obtaining key terms of the model, dynamically determining optimization parameters, and constructing a complete objective function. Compared with traditional methods, this technical solution based on derivative operation and adaptive parameter adjustment significantly improves the separability of interference signals and target signals, and realizes high-precision magnetic compensation without complex maneuvering flight.

[0075] In some embodiments, the step 1022 includes:

[0076] In step 10221, the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value are input into the objective function, and the objective function is solved in each partition to obtain the compensation coefficients of each partition by using the symmetric overrelaxation iteration method.

[0077] In the embodiments of the present application, the system first receives measurement values from the SQUID magnetic gradient meter and the SQUID magnetometer. The computer substitutes these measurement values into the pre-constructed objective function, which includes derivative terms and fitting terms. The system uses the symmetric overrelaxation iteration method for partitioned solution, which accelerates convergence under the premise of ensuring numerical stability by setting appropriate relaxation factors and iteration parameters. During the solution process, the system optimizes the calculation by using different norm orders and weight coefficients for different regions according to the magnetic source region division results. In each iteration, the system updates the compensation coefficient estimate and checks whether the convergence condition is met. When the preset convergence precision or the maximum number of iterations is reached, the system outputs the optimal compensation coefficient solution of the current partition.

[0078] The embodiment of the application realizes high-precision partition parameter solving by the symmetric overrelaxation iterative method, guarantees local optimality, and ensures global consistency by an intelligent splicing algorithm. The technical scheme of partition processing and overall splicing effectively solves the problem of insufficient adaptability of the traditional method in a complex magnetic field environment.

[0079] In some embodiments, after the 10221, further comprising:

[0080] Step 10222, compensating the magnetic field measurement value corresponding to each division zone by using the compensation coefficient to obtain the partition compensation data corresponding to each division zone.

[0081] Step 10223, splicing the partition compensation data to obtain the compensation data of the towed superconducting full-tensor magnetic gradient measurement system.

[0082] In the embodiment of the application, the system first solves the compensation coefficient x by using the symmetric overrelaxation iterative method for each regional data. Then, the magnetic gradient data of each division zone is compensated by using the compensation coefficient of each division zone to obtain the compensated magnetic gradient data of each division zone. Finally, the compensated magnetic gradient data of each division zone is spliced to obtain the compensated measurement data of the entire system.

[0083] The embodiment of the application significantly improves the compensation precision of the superconducting magnetic gradient data by first optimizing the measurement error by the gradient descent method, then implementing intelligent partitioning of the survey line based on the error characteristics, adopting different processing strategies in different regions, and finally integrating the partition parameters into a global compensation scheme. The embodiment is particularly suitable for towed systems that cannot perform complex maneuvering flights, solves the problem of poor compensation effect of the traditional method under weak signal conditions, and provides a more reliable data basis for deep resource exploration.

[0084] In some embodiments, the step 10221 comprises:

[0085] Step 102211, setting the relaxation factor and stopping condition of the symmetric overrelaxation iterative method.

[0086] In the embodiment of the application, the system first initializes the parameters of the symmetric overrelaxation iterative method, including setting the value range (1<ω<2) of the relaxation factor ω. The system selects an appropriate relaxation factor according to the characteristics of the magnetic compensation problem, and the optimal value is usually determined by experiment or experience. At the same time, the system sets the stopping condition parameters, including the maximum number of iterations Nmax and the allowed error threshold ε. These parameters will be used to control the termination time of the iteration process, ensuring that the calculation is stopped when sufficient precision or maximum calculation amount is reached.

[0087] Step 102212, solving the interference compensation model in each partition according to the relaxation factor to obtain the value of the objective function.

[0088] In the embodiment of the present application, the system performs symmetric over-relaxation iterative calculation on the coefficient matrix of the magnetic compensation model according to the set relaxation factor ω. In each iteration, the system calculates the residual of the current solution and adjusts the update step length using the relaxation factor. The system also calculates the value of the objective function, which includes the fitting error between the measured data and the model prediction and the fitting term. Through iterative updating, the system gradually optimizes the compensation coefficients, so that the value of the objective function converges to the optimal solution.

[0089] Step 102213, terminating the iteration when the value of the objective function meets the stop condition to obtain the partition magnetic gradient compensation value.

[0090] In the embodiment of the present application, the system checks the stop condition after each iteration: whether the change in the value of the objective function is less than the preset threshold ε, or whether the number of iterations reaches Nmax. When any of the conditions is met, the system terminates the iteration process and outputs the current optimal compensation coefficients. The system applies these parameters to the original measured data to calculate the compensated magnetic gradient values of each partition. Finally, the system splices the compensation results of all partitions to form a complete compensated magnetic gradient data set.

[0091] The embodiment of the present application realizes efficient solution of the magnetic compensation model by reasonably setting the relaxation factor and the stop condition. The symmetric over-relaxation iterative method ensures the stability and convergence speed of the solution process, and the partition processing strategy adapts to the differences in the characteristics of the magnetic field in different regions.

[0092] In some embodiments, after step 104, the method further comprises:

[0093] Step 201, calculating a first standard deviation of pre-compensation data and a second standard deviation of post-compensation data.

[0094] In the embodiment of the present application, the system first reads and stores the original magnetic gradient measurement data sequence before compensation, and performs arithmetic calculation on these data to obtain the first standard deviation of the pre-compensation data. Then the system reads the data sequence processed by the compensation algorithm, and also performs arithmetic calculation to obtain the second standard deviation of the post-compensation data. The system stores the second standard deviation as the first key indicator for evaluating the compensation effect.

[0095] Step 202, taking the ratio of the first standard deviation and the second standard deviation as the compensation improvement ratio.

[0096] In the embodiments of the present application, the system takes the ratio of the first standard deviation and the second standard deviation as the compensation improvement ratio. The larger the ratio is, the better the compensation effect is and the more obvious the data quality improvement is. The system stores the ratio as the second key indicator for evaluating the compensation effect.

[0097] In step 203, the compensation effect is evaluated based on the compensation improvement ratio.

[0098] In the embodiments of the present application, the system takes the compensation improvement ratio as an input parameter for comprehensive evaluation and analysis. The system establishes an evaluation model to perform weighted calculation on the two indicators or establishes a scoring system to give a quantitative score of the compensation effect. Meanwhile, the system can set a threshold standard to determine whether the compensation effect reaches the expected target. The evaluation result can be used for optimization and adjustment of algorithm parameters or as an output content of the compensation quality report.

[0099] The embodiments of the present application quantify the improvement degree of the compensation algorithm on the overall data quality by calculating the compensation improvement ratio, and evaluate the stability and consistency of the data after compensation by calculating the standard deviation. The evaluation method is particularly suitable for the high-precision weak-signal detection scene of the superconducting magnetic gradient measurement system, and can accurately capture the improvement effect of the compensation processing on the extremely weak magnetic signal, thereby ensuring the reliability and accuracy of the airborne magnetic survey data.

[0100] In some embodiments, the present application is combined with another data compensation method, which is specifically as follows:

[0101] Step S1, interference source analysis

[0102] For the towed low-temperature superconducting full-tensor magnetic gradient measurement system, when the distance between the airplane and the pod is far enough, the magnetic interference of the airplane on the SQUID magnetic gradiometer in the pod can be significantly reduced to a negligible level. This feature provides a reasonable basis for simplifying the magnetic compensation model, so that the model focuses more on the interference of the pod flight platform and the SQUID magnetic gradiometer itself. This part will derive the interference compensation model from the definition of tensor, the measurement principle of SQUID magnetic gradiometer and the structure of superconducting magnetic probe.

[0103] Step S2, analysis of working principle of SQUID gradiometer

[0104] The SQUID magnetic gradiometer is essentially a magnetic flux-voltage converter, which includes signal detection and superconducting loop. The core part is composed of a superconducting loop and a Josephson structure, as shown in FIG. 1. Figure 1 When the magnetic flux in the superconducting loop changes, the superconducting loop will induce a current. Therefore, the SQUID actually measures the difference between the magnetic flux change amounts in the two superconducting loops, and this difference represents the magnetic gradient field with direction. Therefore, the measurement value G of the SQUID magnetic gradiometer can be represented by formula (1).

[0105]

[0106] where Δφ1 and Δφ2 represent the magnetic flux of the two superconducting loops, S is the effective inductive area of the superconducting loop, L is the baseline length of the two superconducting loops, and ΔBi is the change in the magnetic field strength in the i-th superconducting loop.

[0107] The low-temperature superconducting full-tensor magnetic gradient measurement system adopts a regular hexagonal prism structure, six SQUID magnetic gradiometers are installed on the six sides of the hexagonal prism, and a right-hand coordinate system with the center of the bottom surface of the hexagonal prism as the origin is established as shown in FIG. 1, the z-axis is vertically upward, the angle between the side surface and the bottom surface is a, and the azimuth angle β of each surface is β = 60° × n (n = 0, 1,..., 5) as shown in FIG. 2. Figure 3 Figure 3 The relationship between the SQUID gradiometer output and the three-component magnetic field can be further represented by formula (2) through projection decomposition of the magnetic field components in the superconducting loop plane:

[0108]

[0109] where ΔB ix , ΔB iy , and ΔB iz (i = 1, 2) are the changes in the three-component magnetic field in the x, y, and z directions at the i-th superconducting loop, which directly reflects the dynamic changes of the magnetic field in the three directions and further affects the output signal of the SQUID magnetic gradiometer.

[0110] Step S3, establishing a magnetic compensation model

[0111] The superconducting airborne full-tensor magnetic gradient measurement system adopts a soft connection pod design (suspension distance of about 50 meters), which effectively isolates the main magnetic interference of the aircraft platform. Therefore, the interference field generated by the pod at each superconducting loop of the SQUID gradiometer becomes the dominant factor affecting the measurement of the SQUID magnetic gradiometer, and the interference field generated by the pod at each superconducting loop of the SQUID gradiometer can be modeled as a three-component form as shown in formula (3):

[0112]

[0113] where i = 1, 2 represents the two superconducting loops of the SQUID gradiometer, B j (j = x, y, z) are the three components of the earth's magnetic field, B ij (j = x, y, z) are the three components of the interference of the pod at the i-th superconducting loop, P ij , k ijl , and e ijl (j, l = x, y, z) are the residual magnetic coefficient, the magnetic induction coefficient, and the eddy current interference coefficient at the i-th superconducting loop, respectively. ​

[0114] Unlike traditional magnetometers, the SQUID gradiometer's measurement mechanism results in a significantly specific response to disturbance fields:

[0115] 1) Residual magnetism exclusion effect: The flux quantization characteristic of the superconducting ring makes it sensitive only to changing flux. The short-term constant residual magnetism interference (dB / dt=0) will not generate a measurable signal. Therefore, the residual magnetism term does not need to be considered in formula (3).

[0116] 2) Dynamic coupling characteristics: For the two types of interference, namely magnetic induction and eddy current, special attention should be paid to the fact that the SQUID magnetic gradient meter measures the change. Therefore, in the interference model, the remanence is proportional to the first derivative of the magnetic field, and the eddy current interference is proportional to the second derivative of the magnetic field.

[0117] Based on the above analysis, the interference model of the i-th superconducting ring of the SQUID magnetic gradient meter from the pod platform can be expressed as Equation (4).

[0118]

[0119] Where △B ij (j=x,y,z) represents the three magnetic flux variation components of the pod disturbance at the i-th superconducting ring.

[0120] Combining the working principle formulas (2) and (4) of the SQUID gradiometer, the interference model of the SQUID magnetic gradiometer can be obtained as shown in formula (5). The essential difference between this model and the interference model of the traditional magnetometer is that the interference of the SQUID magnetic gradiometer does not include the residual magnetic field interference term, is unrelated to the magnetic field strength itself, but is closely related to the first and second derivative terms of the magnetic field.

[0121]

[0122] Where K represents k ijl The matrix formed, i.e. k yj =sinαcosβ(k 1yj -k 2yj );

[0123] E represents e ijl The burden of proof, namely e yj =sinαcosβ(e 1yj -e 2yj ); (j = x, y, z).

[0124] Due to the limitation of processing technology, the two superconducting rings of the SQUID magnetic gradiometer are difficult to achieve complete symmetry, and the coil normal cannot be guaranteed to be absolutely parallel, resulting in output of pseudo-gradient values in a uniform magnetic field environment. In actual application, a three-axis SQUID magnetometer is usually installed near the SQUID magnetic gradiometer to monitor the magnetic field environment in real time, and the pseudo-gradient value is corrected by minimizing the standard deviation, as shown in formula (6), B ri , (i = x, y, z) is the SQUID three-axis magnetometer value.

[0125]

[0126] where G balance represents the correction compensation value, c i (i = x, y, z) represents the compensation coefficient of the unbalance degree of the three-component magnetic field.

[0127] The SQUID magnetic gradiometer should be zero in theory at the locking moment, but an unknown offset o will appear in actual measurement. Therefore, the SQUID magnetic gradient compensation model can be obtained by combining formula (5) and formula (6), as shown in formula (7), G r is the compensated magnetic gradient value, G m is the gradiometer measurement value, and G m has the same meaning as G in the above formula, A is a matrix composed of the first and second derivatives of the three-component magnetic field and the magnetometer measurement value, and m is the compensation coefficient.

[0128]

[0129] Step S4, constructing an objective function

[0130] The SQUID magnetic gradient data belongs to a typical weak signal measurement system, and the amplitude of the interference signal is close to the order of magnitude of the target signal. Theoretical analysis shows that the interference signal and the flight attitude parameters (such as the pitch angle and the roll angle) show significant correlation, and there is overlap in the frequency domain with the target signal. This frequency spectrum aliasing phenomenon causes the signal-to-noise ratio in the magnetic anomaly area to be significantly reduced, bringing challenges to the extraction of the target signal.

[0131] In order to improve the signal separability, the present application proposes a method based on the time domain differential operator to perform first-order time derivative operation on the measurement signal and the target signal, and to improve the separation effect by enhancing the difference between the interference signal and the target signal. On this basis, the adaptive objective function is constructed as follows formula (8):

[0132]

[0133] DG m-DAm represents derivative information of the fitting data, which can effectively amplify the high-frequency components of the interference signal and suppress the high-frequency noise in the target signal, and can also be used to capture the boundary characteristics of the signal;

[0134] a and b represent adaptive weight coefficients, which can dynamically balance the contributions of the fitting terms according to the local magnetic field characteristics;

[0135] p and q represent variable norm orders, and the variable order enhances the robustness of the model to outliers.

[0136] Step S5, magnetic source region division

[0137] The magnetic compensation method proposed in the application uses a two-step method to directly solve the compensation coefficients in different regions, and the specific method is as follows:

[0138] 1) Calculate the initial compensation data G using the least squares fitting result r0 However, the accuracy of the initial compensation result is low, so an error prediction model based on data driving is introduced, the system error v in the least squares calculation is learned and corrected, and a more accurate initial compensation result is obtained, as shown in formulas (9) and (10).

[0139] v = G m -G r0 (9);

[0140] G corrected = G r0 + vh (10);

[0141] Where h is the correction coefficient, which is optimized by gradient descent method. After multiple optimizations, the machine change rate of the calculation error v Construct the feature matrix

[0142] 2) Set threshold values t1 and t2, divide the region according to the following formula, and use different fitting orders p, q and weight coefficients in different regions, as shown in the following formula (11):

[0143]

[0144] In the low error area, in order to retain the fluctuation amplitude and shape of the signal, b can be appropriately increased to increase the proportion of signal derivative information; while in the low error area, in order to reduce the invalid fluctuation, a can be appropriately increased to increase the proportion of data fitting.

[0145] Step S6, compensation coefficient solving

[0146] In order to ensure the stability of the solution, the symmetric overrelaxation iteration method is used to solve the compensation coefficient m, and the solving steps are as follows:

[0147] 1) Initialize the coefficient matrix A = (a ij );

[0148] 2) Give the initial value m0, relaxation factor ω, maximum iteration number k, and stop error v;

[0149] 3) Perform iterative calculation according to the following formula (12);

[0150]

[0151] 4) Determine whether the iteration is stopped, if ||m k+1 -x k || / ||x k ||<ε, the iteration is stopped, and ε represents a preset loss value, otherwise return to 3), or reach the maximum iteration number;

[0152] 5) Calculate the compensated data according to the compensation coefficient m.

[0153] When the symmetric over-relaxation iteration method is used to solve the compensation coefficient m i of each segment of data, the corresponding compensated magnetic gradient data G ri is calculated, and finally each segment of compensated data is spliced to obtain the compensated magnetic data G r , see formula (13), wherein n is the number of magnetic source regions.

[0154] G r ={G r1 ,G r2 ,…,G rn} (13);

[0155] Step S7, compensation effect evaluation

[0156] The compensation improvement ratio (IR) and the first and second standard deviations (SD) are used to quantitatively evaluate the compensation results (calculation methods are shown in formulas (14) and (15)). The IR reflects the degree of improvement of signal quality before and after compensation, and the SD represents the discrete characteristics of the compensated data.

[0157]

[0158] wherein d 0 is the data before compensation, mean(d 0 ) is the average value of the data before compensation, d c is the data after compensation, mean(d c ) is the average value of the data after compensation, and N is the number of data.

[0159] In some embodiments, this application also provides another data compensation method, specifically including:

[0160] Step P1, System Configuration

[0161] To verify the effectiveness of the compensation method proposed in this application in actual flight measurements, a systematic flight experiment was designed and implemented. All components of the cryogenic supernavigation full-tensor magnetic gradient measurement system used in the experiment were integrated into a dedicated pod, which was suspended below the helicopter by cables for flight measurements. A schematic diagram of the pod system configuration is shown below. Figure 4 As shown. The core measurement device of the experiment is the SQUID sensor measurement probe, which integrates six SQUID gradiometers and one triaxial SQUID magnetometer. To ensure measurement accuracy, the SQUID magnetic gradiometers are encapsulated in a non-magnetic Dewar flask filled with liquid helium, allowing them to maintain a superconducting state at a temperature of 4.2K, thereby ensuring optimal measurement performance.

[0162] Step P2, Flight Data Acquisition Scheme

[0163] As a flight test area, the background magnetic field in this region is generally stable, but there are localized areas affected by human activities such as industrial facilities.

[0164] The activity caused a magnetic anomaly. Ten north-south survey lines were set up for the flight experiment, with a line spacing of 500 meters and a single line length of 10 kilometers, maintaining a flight altitude of 200 meters. To compare with traditional compensation methods, a square closed-loop flight was also conducted at an altitude of 1000 meters to obtain the parameter calculation data required by traditional compensation methods. The flight path was cut after invalid survey lines were removed, as shown below. Figure 5 As shown.

[0165] Step P3, Analysis of Compensation Results

[0166] This application employs both traditional compensation methods and the method proposed in this application to process flight data, and systematically evaluates their compensation effects. The traditional compensation method calculates compensation coefficients based on closed-loop flight path data at 1000 meters altitude and applies them to survey line data at 200 meters altitude. In contrast to the traditional method, the novel compensation method proposed in this application does not rely on high-altitude flight data but directly processes data based on 200-meter survey line data. Figure 6 The compensation effects of the two methods are visually demonstrated: the red curve represents the preprocessed magnetic gradient data, which still has obvious interference despite initial noise reduction; the green and blue curves correspond to the compensation results of the traditional method and the method of this application, respectively. It can be seen that the data fluctuation after compensation by the method of this application is significantly reduced, showing better stability and anti-interference ability.

[0167] Table 1 statistics the compensation results of the two methods. Compared with the traditional method, the proposed method significantly improves the IR value of each tensor component (increases by 2.53, 3.09, 1.94, 2.14, 2.13, 1.87 times, respectively, with an average of 2.28), while the SD value is significantly reduced (reduces to 67.60%, 42.00%, 51.72%, 40.81%, 43.66%, 63.08%, respectively, with an average of 51.48%). These data fully prove that the proposed method is superior to the traditional method in improving data accuracy and compensation stability.

[0168] Table 1

[0169]

[0170] The magnetic compensation model established by the embodiments of the present application comprehensively considers the measurement platform interference and the SQUID gradiometer self interference, and shows high innovation and practicality. The model starts from the core measurement principle of the SQUID gradiometer, and through comprehensive and in-depth derivation and optimization, successfully eliminates the influence of residual magnetism and other stability interference on the measurement accuracy of the SQUID gradiometer, and ingeniously introduces the first and second order derivative terms of the magnetic field, which more accurately reflects the real situation of the magnetic field change, which is a major breakthrough in traditional magnetic compensation technology, provides a more reliable basis for subsequent processing and analysis of magnetic measurement data, and opens up a new way for the further development and application of magnetic measurement technology.

[0171] The embodiments of the present application propose a new magnetic compensation method, which fully considers the working characteristics of the towed system and the characteristics of the SQUID magnetic sensor losing lock under complex motion, and the core innovation is to break through the dependence of the traditional magnetic compensation method on complex maneuvering flight motion, realize the significant reduction of flight cost, the significant simplification of operation process and the comprehensive improvement of field flight operation safety. In order to ensure the reliability of the method in practical application, a weak signal separation technology is designed to significantly enhance the difference between the interference signal and the target signal, thereby significantly improving the efficiency of signal separation and extraction, and a corresponding compensation coefficient solving method is designed to realize accurate solving of the compensation coefficient without complex maneuvering flight data constraints. This innovation provides a new magnetic compensation solution for the towed superconducting airborne magnetic measurement system, significantly improves the engineering practicability and operation safety of the system, and shows important practical value in practical application.

[0172] Based on the foregoing embodiments, the embodiments of the present application provide a data compensation device, which comprises units and modules included in the units, and can be realized by a processor in a computer device. Of course, the device can also be realized by a specific logic circuit. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA).

[0173] Figure 7 A schematic diagram of the composition structure of the data compensation device provided by the embodiments of the present application is shown in FIG. 1. Figure 7 As shown in FIG. 1, the data compensation device comprises:

[0174] The construction module 301 is configured to establish a target interference compensation model of the towed superconducting full-tensor magnetic gradient measurement system for the gondola platform, and the target interference compensation model at least comprises a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes a residual magnetic interference term of the gondola platform, and the interference compensation term comprises a magnetosensitive interference term represented by a first-order derivative of an earth magnetic field component and an eddy current interference term represented by a second-order derivative term of the earth magnetic field component. The sensor self-interference correction term is configured to represent an unbalance degree and an unknown offset of the SQUID magnetic gradiometer. A target function is constructed based on the target interference compensation model, and input parameters of the target function comprise a SQUID magnetic gradiometer measurement value and a SQUID magnetometer measurement value.

[0175] The compensation module 302 is configured to input the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value into the target function, solve the compensation coefficient by using a symmetric overrelaxation iteration method, and compensate the towed superconducting full-tensor magnetic gradient measurement system based on the compensation coefficient.

[0176] In some embodiments, the construction module 301 is further configured to obtain a conventional interference compensation model of the towed superconducting full-tensor magnetic gradient measurement system for the gondola platform, and the conventional interference compensation model at least comprises a residual magnetic interference coefficient, a magnetosensitive interference coefficient, and an eddy current interference coefficient. The residual magnetic interference coefficient is excluded from the conventional interference compensation model, and the magnetosensitive interference coefficient is derived as a magnetosensitive interference term represented by a first-order derivative of an earth magnetic field component, and the eddy current interference coefficient is derived as an eddy current interference term represented by a second-order derivative term of the earth magnetic field component.

[0177] The sensor self-interference correction term characterized by the SQUID magnetometer measurement value is introduced into the conventional interference compensation model to obtain a target interference compensation model.

[0178] In some embodiments, the construction module 301 is further configured to: obtain a fitting term and a derivative term of the target interference compensation model; determine adaptive weight coefficients and variable norm orders corresponding to the fitting term and the derivative term, respectively; and combine the fitting term and the derivative term with the corresponding adaptive weight coefficients and variable norm orders to obtain a target function.

[0179] In some embodiments, the compensation module 302 is further configured to: input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the target function, and solve the target function in each division zone by using a symmetric overrelaxation iteration method to obtain compensation coefficients of each division zone.

[0180] In some embodiments, the compensation module 302 is further configured to: compensate the measurement value corresponding to each division zone by using the compensation coefficients to obtain division compensation data corresponding to each division zone; and splice the division compensation data to obtain compensation data of the towed superconducting full-tensor magnetic gradient measurement system.

[0181] The division compensation coefficients are spliced based on the division result to obtain compensation coefficients.

[0182] In some embodiments, the compensation module 302 is further configured to:

[0183] In some embodiments, the compensation module 302 is further configured to:

[0184] In some embodiments, the compensation module 302 is further configured to: calculate a first standard deviation of the pre-compensation data and a second standard deviation of the post-compensation data; take a ratio of the first standard deviation and the second standard deviation as a compensation improvement ratio; and evaluate the compensation effect based on the compensation improvement ratio.

[0185] The interference compensation model is established specially for the towed superconducting full-tensor magnetic gradient measurement system, the interference compensation model accurately describes the interference mechanism specific to the SQUID magnetic gradiometer by excluding the residual magnetic interference term of the pod platform, introducing the magnetosensitive interference term represented by the first-order derivative of the earth magnetic field component and the eddy current interference term represented by the second-order derivative term of the earth magnetic field component, adapts to the soft connection characteristics of the towed system, realizes high-precision compensation of the superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0186] The above device embodiments are similar to the description of the above method embodiments, and have similar beneficial effects as the method embodiments. In some embodiments, the device provided by the embodiments of the present application has functions or includes modules that can be used to perform the methods described in the above method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application.

[0187] It should be noted that, in the embodiments of the present application, if the above-mentioned data compensation method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various storage media that can store program codes. Thus, the embodiments of the present application are not limited to any specific hardware, software or firmware, or any combination of hardware, software and firmware.

[0188] The embodiments of the present application provide a computer device, including a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements part or all of the steps in the above method when executing the program.

[0189] The embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement part or all of the steps in the above method. The computer-readable storage medium can be transitory or non-transitory.

[0190] The embodiment of the present application provides a computer program, including computer readable code, wherein when the computer readable code runs in a computer device, a processor in the computer device executes part or all steps of the above method.

[0191] The embodiment of the present application provides a computer program product, including a non-transitory computer readable storage medium storing a computer program, wherein when the computer program is read and executed by a computer, part or all steps of the above method are implemented. The computer program product can be specifically implemented by means of hardware, software or combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium, and in some other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK) or the like.

[0192] It should be noted that: the above description of various embodiments tends to emphasize the differences between various embodiments, and the same or similar parts can be mutually referred. The above description of the device, storage medium, computer program and computer program product embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the device, storage medium, computer program and computer program product embodiments of the present application, please refer to the description of the method embodiments of the present application.

[0193] It should be noted that, Figure 8 A hardware entity of a computer device in the embodiment of the present application is shown in FIG. 7, which includes one or more processors 701, a communication interface 702 and a memory 703, wherein: Figure 8 The hardware entity of the computer device 700 includes one or more processors 701, a communication interface 702 and a memory 703, wherein:

[0194] The processor 701 generally controls the overall operation of the computer device 700.

[0195] The communication interface 702 can enable the computer device to communicate with other terminals or servers through a network.

[0196] The memory 703 is configured to store instructions and applications executable by the processor 701, and can also cache data to be processed by the processor 701 and modules in the computer device 700 (for example, image data, audio data, voice communication data and video communication data), which can be implemented by FLASH or random access memory (RAM). The processor 701, the communication interface 702 and the memory 703 can transmit data through the bus 704. Each processor 100 includes one or more cores.

[0197] It should be noted that the computer device can include multiple processors 701, and each processor 701 can interact with each other through all-to-all, allgather or allreduce aggregation communication mode. The processor 701 can be a central processing unit (CPU), a graphics processing unit (GPU), a neural-network processing units (NPU), a tensor processing unit (TPU), a data processing units (DPU), an accelerated processing unit (APU), a floating processing units (FPU) or an application-specific integrated circuit (ASIC), etc. The processor can also be a single-core processor or a multi-core processor. The processor can be a combination of a CPU and a hardware chip. The hardware chip can be an ASIC, a PLD or a combination thereof. The PLD can be a complex programmable logic device (CPLD), an FPGA, a generic array logic (GAL) or any combination thereof. The processor can also be implemented by a logic device with built-in processing logic, such as an FPGA or a digital signal processor (DSP), etc.

[0198] The communication interface 702 can be a wired interface or a wireless interface, for communicating with other modules or devices. The wired interface can be an Ethernet interface, a local interconnect network (LIN), etc. The wireless interface can be a cellular network interface or a wireless local area network interface, etc.

[0199] The memory 703 can be a non-volatile memory, for example, a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The memory 703 can also be a volatile memory, which can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, for example, a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous DRAM (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a synchlink DRAM (SLDRAM), and a direct rambus RAM (DRRAM), a direct rambus dynamic RAM (DRDRAM), and a rambus DRAM (RDRAM).

[0200] The bus 704 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0201] It should be understood that every feature and combination of features that is described above in relation to one embodiment is applicable to at least one other embodiment, unless specifically stated otherwise. It should also be understood that every embodiment described above can be combined with any other embodiment unless specifically stated otherwise.

[0202] It should be noted that, in the present application, the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusions, such that a process, method, article, or device that comprises a list of elements does not necessarily include those elements only, but can include other elements not expressly listed, or can include elements inherent in such process, method, article, or device. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element.

[0203] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each component part shown or discussed can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0204] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0205] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0206] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the method embodiments when executed; and the foregoing storage medium includes a mobile storage device, a read only memory (ROM), a magnetic disc or an optical disc, and various storage medium capable of storing program codes.

[0207] Alternatively, the integrated units of the present application can be stored in a computer readable storage medium if the integrated units are realized in the form of software function modules and sold or used as independent products. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes a mobile storage device, a ROM, a magnetic disc or an optical disc, and various storage medium capable of storing program codes.

[0208] The above is only an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application.

Claims

1. A data compensation method, characterized by, The method comprises: establishing a target interference compensation model of the towed superconducting full tensor magnetic gradient measurement system on the gondola platform, the target interference compensation model at least comprising: a SQUID magnetic gradiometer measurement term, an interference compensation term and a sensor self-interference correction term; wherein the interference compensation term excludes a residual magnetic interference term of the gondola platform, and the interference compensation term comprises: a magnetosensitive interference term represented by a first-order derivative of a component of the earth's magnetic field, and an eddy current interference term represented by a second-order derivative term of the component of the earth's magnetic field; the sensor self-interference correction term is used to represent an imbalance degree and an unknown offset of the SQUID magnetic gradiometer itself; constructing a target function based on the target interference compensation model; inputting the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value into the target function, and obtaining compensation coefficients by using a symmetric overrelaxation iteration method; compensating the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficients.

2. The method of claim 1, wherein, The method comprises: obtaining a traditional interference compensation model of the towed superconducting full tensor magnetic gradient measurement system on the gondola platform, the traditional interference compensation model at least comprising: a residual magnetic interference coefficient, a magnetosensitive interference coefficient and an eddy current interference coefficient; excluding the residual magnetic interference coefficient from the traditional interference compensation model, and deriving the magnetosensitive interference coefficient into a magnetosensitive interference term represented by a first-order derivative of a component of the earth's magnetic field, and deriving the eddy current interference coefficient into an eddy current interference term represented by a second-order derivative term of the component of the earth's magnetic field; introducing a sensor self-interference correction term represented by the SQUID magnetometer measurement value into the traditional interference compensation model to obtain a target interference compensation model.

3. The method of claim 1, wherein, The method comprises: obtaining a fitting term and a derivative term of the target interference compensation model; determining adaptive weight coefficients and variable norm orders corresponding to the fitting term and the derivative term respectively; combining the fitting term and the derivative term with the corresponding adaptive weight coefficients and variable norm orders to obtain a target function.

4. The method of claim 3, wherein, The method comprises: inputting the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value into the target function, and obtaining compensation coefficients by using a symmetric overrelaxation iteration method in each division zone.

5. The method of claim 4, wherein, The method comprises: compensating measurement values corresponding to each division zone by using the compensation coefficients to obtain division zone compensation data corresponding to each division zone; splicing the division zone compensation data to obtain compensation data of the towed superconducting full tensor magnetic gradient measurement system.

6. The method according to claim 4 or 5, characterized in that, The method comprises: setting a relaxation factor and a stop condition of the symmetric overrelaxation iteration method; Solving the interference compensation model according to the relaxation factor in each partition to obtain a value of the objective function; Terminating iteration when the value of the objective function meets the stop condition to obtain a compensation coefficient corresponding to each partition.

7. The method according to any one of claims 1 to 5, characterized in that, After compensating the towed superconducting full-tensor magnetic gradient measurement system based on the compensation coefficient, the method further includes: Calculating a first standard deviation of pre-compensation data and a second standard deviation of post-compensation data; Taking a ratio of the first standard deviation and the second standard deviation as a compensation improvement ratio; Evaluating compensation effect based on the compensation improvement ratio.

8. A data compensation device, characterized by comprising: The method includes: A construction module is configured to establish a target interference compensation model of a towed superconducting full-tensor magnetic gradient measurement system by a pod platform, the target interference compensation model including at least a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term; wherein the interference compensation term excludes a residual magnetic interference term of the pod platform, and the interference compensation term includes a magnetic induction interference term represented by a first-order derivative of an earth magnetic field component and an eddy current interference term represented by a second-order derivative term of the earth magnetic field component; the sensor self-interference correction term is used to represent an imbalance degree and an unknown offset of the SQUID magnetic gradiometer; a target function is constructed based on the target interference compensation model, wherein input parameters of the target function include SQUID magnetic gradiometer measurement values and SQUID magnetometer measurement values; A compensation module is configured to input the SQUID magnetic gradiometer measurement values and the SQUID magnetometer measurement values into the target function, and solve the compensation coefficient by using a symmetric overrelaxation iteration method; and the towed superconducting full-tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

9. A computer device comprising a memory and a processor, the memory storing a computer program capable of running on the processor, characterized in that, The processor implements the steps in the data compensation method of any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the steps in the data compensation method of any one of claims 1 to 7 when executed by the processor.

Images