Ferromagnetic target positioning method and device

Abstract

The present application discloses a method and device for locating a ferromagnetic target, the method comprising: obtaining the magnetic induction intensity generated by a ferromagnetic target at a measuring point through a sensor array, and determining the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity; performing single-point positioning processing based on the face-center magnetic gradient tensor to obtain candidate values ​​of positioning parameters; creating a nonlinear equation group based on the body-center magnetic gradient tensor and a magnetic dipole model, the nonlinear equation group representing the mapping relationship between the magnetic gradient tensor difference and the positioning parameters; taking the value of the magnetic gradient tensor difference satisfying a preset requirement as an optimization target, and based on a preset optimization algorithm and the nonlinear equation group, correcting the candidate values ​​of the positioning parameters to obtain the target values ​​of the positioning parameters; and determining the unbiased positioning result of the ferromagnetic target based on the target values ​​of the positioning parameters.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and in particular to a ferromagnetic target positioning method and device. Background Art

[0002] The geomagnetic anomaly detection method is the earliest developed and widely used passive detection method, with the advantages of good stability, fast response speed, and non-invasive detection. The geomagnetic anomaly characteristics are not affected by media such as soil and water, and can effectively detect underground ferromagnetic targets. When a ferromagnetic target is in the geomagnetic field, it will be magnetized by the geomagnetic field, thereby generating a magnetic anomaly field. The superposition of the magnetic anomaly field and the geomagnetic field will cause the uniform geomagnetic field to be distorted, thereby generating geomagnetic anomalies. The positioning of ferromagnetic targets plays an important role in the geological exploration process.

[0003] However, the current process of locating ferromagnetic targets by analyzing magnetic anomaly signals does not take into account the changes in aspheric parameters, resulting in large errors in positioning results and low accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a ferromagnetic target positioning method and device, which is used to solve the problems of large positioning result errors and low accuracy in the current positioning process of ferromagnetic targets.

[0005] In order to achieve the above objectives, the present application embodiment adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a ferromagnetic target positioning method, comprising:

[0007] Acquire the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determine the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity;

[0008] Performing single-point positioning processing based on the face-centre magnetic gradient tensor to obtain candidate values ​​of positioning parameters;

[0009] Based on the body-centered magnetic gradient tensor and the magnetic dipole model, a nonlinear equation group is created, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters;

[0010] Taking the value of the magnetic gradient tensor difference satisfying a preset requirement as an optimization goal, based on a preset optimization algorithm and the nonlinear equation group, modifying the candidate value of the positioning parameter to obtain a target value of the positioning parameter;

[0011] Based on the target value of the positioning parameter, an unbiased positioning result of the ferromagnetic target is determined.

[0012] In a second aspect, an embodiment of the present application provides a ferromagnetic target positioning device, comprising:

[0013] An acquisition unit, used for acquiring the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determining the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity;

[0014] A processing unit, configured to perform single-point positioning processing based on the face-centre magnetic gradient tensor to obtain candidate values ​​of positioning parameters;

[0015] A creating unit, configured to create a nonlinear equation group based on the body-centered magnetic gradient tensor and the magnetic dipole model, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters;

[0016] A correction unit, configured to take the value of the magnetic gradient tensor difference satisfying a preset requirement as an optimization target, and based on a preset optimization algorithm and the nonlinear equation group, correct the candidate value of the positioning parameter to obtain a target value of the positioning parameter;

[0017] A determination unit is used to determine an unbiased positioning result of the ferromagnetic target based on a target value of the positioning parameter.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in the first aspect.

[0020] At least one of the above-mentioned technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: in order to improve the accuracy of ferromagnetic target positioning, the magnetic induction intensity generated by the ferromagnetic target at the measuring point is obtained through the sensor array, and the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array are determined based on the magnetic induction intensity, and single-point positioning processing is performed based on the face-center magnetic gradient tensor to obtain candidate values ​​of the positioning parameters, and a nonlinear equation group is created based on the body-center magnetic gradient tensor and the magnetic dipole model. The nonlinear equation group represents the mapping relationship between the magnetic gradient tensor difference and the positioning parameters, and the value of the magnetic gradient tensor difference that meets the preset requirements is taken as the optimization target, and based on the preset optimization algorithm and the nonlinear equation group, the candidate values ​​of the positioning parameters are corrected to obtain the target values ​​of the positioning parameters, and based on the target values ​​of the positioning parameters, the unbiased positioning result of the ferromagnetic target is determined, which can reduce the positioning error and improve the positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 A schematic diagram of a flow chart of a ferromagnetic target positioning method provided for one embodiment of the present application;

[0023] Figure 2 A schematic diagram of an application scenario structure of a ferromagnetic target positioning method provided for an embodiment of the present application;

[0024] Figure 3 A schematic diagram showing the positioning results of a ferromagnetic target provided by one embodiment of the present application;

[0025] Figure 4 A schematic structural diagram of a ferromagnetic target positioning device provided in one embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of an electronic device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0028] The terms "first", "second", etc. in this specification and claims are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this way can be interchanged where appropriate so that the embodiments of the present application are implemented in a sequence other than those illustrated or described herein. In addition, "and / or" in this specification and claims means at least one of the connected objects, and the character " / " generally means that the objects associated with each other are in an "or" relationship.

[0029] In order to solve the problems of large positioning result errors and low accuracy in the current positioning process of ferromagnetic targets by analyzing magnetic anomaly signals, the embodiment of the present application provides a method for positioning ferromagnetic targets, which obtains the magnetic induction intensity generated by the ferromagnetic target at the measuring point through a sensor array, and determines the face-center magnetic gradient tensor and body-center magnetic gradient tensor of the sensor array, performs single-point positioning processing based on the face-center magnetic gradient tensor, obtains candidate values ​​of positioning parameters, creates a nonlinear equation group based on the body-center magnetic gradient tensor and the magnetic dipole model, and the nonlinear equation group represents the mapping relationship between the magnetic gradient tensor difference and the positioning parameters. The value of the magnetic gradient tensor difference that meets the preset requirements is taken as an optimization target, and based on the preset optimization algorithm and the nonlinear equation group, the candidate values ​​of the positioning parameters are corrected to obtain the target values ​​of the positioning parameters. Based on the target values ​​of the positioning parameters, the unbiased positioning result of the ferromagnetic target is determined, which can reduce the positioning error and improve the positioning accuracy.

[0030] Specifically, the technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0031] See also Figure 1 , is a flow chart of a ferromagnetic target positioning method provided by an embodiment of the present application, the method may include:

[0032] S102, obtaining the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determining the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity.

[0033] Ferromagnetic targets refer to objects or targets with ferromagnetic properties. Ferromagnetic properties are a property of a material. For example, metals such as iron, nickel, cobalt and their alloys will show strong magnetic response after being magnetized under the action of an external magnetic field. Magnetic induction intensity is a physical quantity that describes the strength of the magnetic field. It has magnitude and direction and is usually represented by the letter B.

[0034] The sensor array is composed of a plurality of sensors arranged and distributed, and the sensor array is placed at a measuring point to measure the magnetic induction intensity generated by a ferromagnetic target at the measuring point. In practical applications, sensors of different models and materials can be used for measurement, and the example embodiments of the present application are not limited to this. As an example, the sensor array can be obtained by arranging and distributing a plurality of three-axis fluxgate sensors, which can simultaneously measure the magnetic induction intensity components of the magnetic field on the X, Y and Z axes in the reference coordinate system, so as to facilitate subsequent differential processing and obtain the face-centered magnetic gradient tensor and body-centered magnetic gradient tensor of the sensor array.

[0035] The magnetic gradient tensor describes the magnetic field gradient, that is, the rate and direction of magnetic field change. In three-dimensional space, the magnetic gradient tensor consists of 9 magnetic gradient tensor components forming a 3×3 matrix, such as: Among them, there are 5 independent components. The magnetic gradient tensor components represent the magnetic field gradients of different magnetic induction intensity components on the corresponding coordinate axes. The face-center magnetic gradient tensor is the magnetic gradient tensor at the face center. The face center refers to the face center of the plane formed by the multiple sensors that make up the sensor array. The body-center magnetic gradient tensor is the magnetic gradient tensor at the body center. The body center refers to the center of the centrally symmetrical three-dimensional figure formed by the multiple sensors that make up the sensor array.

[0036] As an optional implementation, when the sensor array is composed of a plurality of sensors and the plurality of sensors are distributed in a cubic shape, the above S102 determines the face-centered magnetic gradient tensor and the body-centered magnetic gradient tensor of the sensor array based on the magnetic induction intensity, including:

[0037] S202, determining the magnetic induction intensity components of the magnetic induction intensity measured by each sensor in the directions of each coordinate axis in the target three-dimensional coordinate system, where the target three-dimensional coordinate system is a three-dimensional coordinate system established with the body center as the origin, and the body center refers to the center of a cube formed by the arrangement of the multiple sensors.

[0038] S204, performing differential processing based on the magnetic induction intensity components to obtain a plurality of magnetic gradient tensor components at the body center and a plurality of magnetic gradient tensor components corresponding to each of a plurality of surface centers, where the surface center refers to the center of the plane where the plurality of sensors are located.

[0039] S206, arranging multiple magnetic gradient tensor components of the body center to obtain a body center magnetic gradient tensor;

[0040] S208, arranging the multiple magnetic gradient tensor components corresponding to the multiple surface centers respectively, to obtain the surface center magnetic gradient tensors corresponding to the multiple surface centers respectively.

[0041] It should be understood that the multiple sensors of the sensor array are symmetrically distributed. As an example, the sensor array may be composed of 8 sensors distributed in a cube, and the 8 sensors are symmetrical about the center of the cube. For example: Figure 2 A schematic diagram of an application scenario structure of a ferromagnetic target positioning method provided by an embodiment of the present application is as follows: Figure 2 In the sensor array model shown in FIG, eight sensors S1, S2, S3, S4, S5, S6, S7, and S8 are distributed at eight vertices of the model. A three-dimensional coordinate system is established with the center of the model as the coordinate origin, namely, the target three-dimensional coordinate system. In the three-dimensional coordinate system, the magnetic induction intensity measured by the sensor S1 is represented by B1, and the magnetic induction intensity components in the X, Y, and Z axis directions are represented by B 1x , B 1y and B 1z The same is true for the remaining sensors, and the center of the surface includes the center of the surface of six planes, for example: the center of the surface of the plane formed by sensors S1, S2, S3 and S4.

[0042] Based on the difference processing of the magnetic induction intensity components of each sensor in different coordinate axis directions, multiple magnetic gradient tensor components corresponding to the body center and multiple magnetic gradient tensor components corresponding to each face center are obtained and arranged to obtain the body center magnetic gradient tensor and each face center magnetic gradient tensor. For example: for the 9 magnetic gradient tensor components corresponding to the body center obtained after the difference processing, 5 independent components are determined and arranged into a matrix in the following form: The body-centered magnetic gradient tensor is obtained, and the magnetic gradient tensors at the centers of each surface are similar.

[0043] The embodiment of the present application performs differential processing based on the magnetic induction intensity component, which can eliminate the interference of the external magnetic field, and then obtain the magnetic gradient tensor at the center of the face and the center of the body more accurately, making the subsequent calculation results more accurate and improving the accuracy of ferromagnetic target positioning. The embodiment of the present application will further explain the above differential processing.

[0044] Specifically, as an optional implementation, the above S204 includes: calculating a first magnetic induction intensity average value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane on the same coordinate axis, and a second magnetic induction intensity average value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane and adjacent to each other on the same coordinate axis;

[0045] Based on the ratio between the first magnetic induction intensity average value and the preset baseline length, a plurality of magnetic gradient tensor components at the body center are obtained;

[0046] Based on the ratio between the second magnetic induction intensity average value and the preset baseline length, a plurality of magnetic gradient tensor components of the surface center are obtained.

[0047] The preset baseline length is related to the edge length of the central symmetrical three-dimensional figure formed by the multiple sensors of the sensor array, for example: Figure 2 In the sensor array model shown in , the baseline length is the side length of the cube. In practical applications, the baseline length can be set according to specific circumstances to build the sensor array and its model in the embodiment of the present application, and the embodiment of the present application does not limit this.

[0048] It should be understood that all components can be obtained by simply calculating five independent magnetic gradient tensor components based on the measured magnetic induction intensity. Example A: Combining the above Figure 2 For the sensor array model shown in , the five independent components of the body-centered magnetic gradient tensor are calculated using the following formulas:

[0049] Where d is the preset baseline length, B xx Take B as an example. xx It represents the magnetic field gradient of the magnetic induction intensity component in the X-axis direction at the center of the body, B x,+X / 4, that is, the first mean value of magnetic induction intensity, which represents the average value of the magnetic induction intensity components of the four sensors S1, S4, S5 and S8 in the plane where the positive direction of the X-axis is located, that is, B x,+X =B 1x +B 4x +B 5x +B 8x , B x,+X / 4d, i.e., the ratio between the first magnetic induction intensity mean value and the preset baseline length; B x,-X / 4, that is, the first magnetic induction intensity mean, which represents the average value of the magnetic induction intensity components of the four sensors on the plane in the negative direction of the X-axis, namely S2, S3, S6 and S7 in the X-axis direction, that is, B x,-X =B 2x +B 3x +B 6x +B 7x , B x,-X / 4d, that is, the ratio between the first magnetic induction intensity mean value and the preset baseline length, and the other components are similar.

[0050] Example B: Combining the above Figure 2 In the sensor array model shown in FIG, the five independent components of the magnetic gradient tensor at the center of the plane where the positive direction of the Z axis is located, that is, the plane formed by sensors S55, S6, S7 and S8, are calculated by the following formulas:

[0051] Where d is the preset baseline length, B xx Take B as an example. xx It represents the magnetic field gradient of the magnetic induction intensity component of the surface center on the X-axis in the X-axis direction, (B 5x +B 8x ) / 2, i.e., the second magnetic induction intensity mean value, which represents the average value of the magnetic induction intensity components of two adjacent sensors S5 and S8 on the plane in the X-axis direction; (B 5x +B 8x ) / 2d, i.e., the ratio between the second magnetic induction intensity mean value and the preset baseline length;

[0052] For example, taking the plane where the negative direction of the Z axis is located, that is, the plane formed by sensors S1, S2, S3 and S4, the five independent components of the magnetic gradient tensor at the center of the plane are calculated by the following formula:

[0053] Similarly, the components of the face-centre magnetic gradient tensor of the remaining planes are obtained.

[0054] The embodiment of the present application calculates the magnetic field gradient of the corresponding magnetic induction intensity component on the corresponding coordinate axis through differential processing, and obtains the magnetic gradient tensor components at the body center and the surface center of the sensor array, which can eliminate the interference of the background magnetic field and make the positioning result of the ferromagnetic target more accurate.

[0055] S104, performing single-point positioning processing based on the face-centre magnetic gradient tensor to obtain candidate values ​​of positioning parameters.

[0056] The positioning parameters include the position vector r and its components r in the X, Y and Z directions. x 、r y and r z , including the magnetic moment vector M and its components m on the X, Y and Z axes x 、m y and m z By performing single-point positioning processing on the centroid magnetic gradient tensor, a candidate value of the positioning parameter is obtained. The present application embodiment will illustrate the above single-point positioning.

[0057] Specifically, as an optional implementation, the above S104 includes: for each face-center magnetic gradient tensor, based on the sum of squares of multiple face-center magnetic gradient tensor components corresponding to the face-center magnetic gradient tensor, obtaining a magnetic gradient tensor invariant corresponding to the face-center magnetic gradient tensor;

[0058] Calculating the difference between multiple magnetic gradient tensor invariants corresponding to multiple face-center magnetic gradient tensors;

[0059] Based on the ratio between the difference value and the preset baseline length, a spatial gradient is obtained;

[0060] The spatial gradient is normalized to obtain the position vector of the ferromagnetic target;

[0061] Based on the position vector and the body-centre magnetic gradient tensor, candidate values ​​of the localization parameters are obtained.

[0062] For each face-center magnetic gradient tensor, the magnetic gradient tensor invariant corresponding to the face-center magnetic gradient tensor is obtained based on the square sum of multiple face-center magnetic gradient tensor components corresponding to the face-center magnetic gradient tensor, wherein the magnetic gradient tensor invariant is calculated by the following formula:

[0063] Among them, C T represents the magnetic gradient tensor invariant, G ij represents the surface center magnetic gradient tensor component, i and j are 1, 2, and 3 respectively, and the following formula is obtained after expansion: For example: Combine Figure 2In the sensor array model shown in , taking the plane opposite to the positive direction of the X-axis, that is, the plane formed by sensors S1, S4, S5 and S8 as an example, the magnetic gradient tensor invariant corresponding to the center magnetic gradient tensor of the plane is calculated by the following formula:

[0064] Among them, the 9 face-centered magnetic gradient tensor components B xx , B xy , B xz , B yx , B yy , B yz , B zx , B zy , and B zz The 9 magnetic gradient tensor components of the face-center magnetic gradient tensor of the plane are represented. The other planes are similar. Then, the magnetic gradient tensor invariants corresponding to the face-center magnetic gradient tensor of each face center are obtained as follows: T,+X , C T,-X , C T,+Y , C T,-Y , C T,+Z and C T,-Z .

[0065] Based on the ratio between the difference and the preset baseline length, the spatial gradient is obtained, wherein the spatial gradient is calculated by the following formula:

[0066] in, represents the spatial gradient, d is the preset baseline length, and i, j, k represent the direction vectors of the X, Y, and Z axes in the spatial coordinate system, respectively.

[0067] The spatial gradient is normalized to obtain the position vector of the ferromagnetic target, where the position vector is calculated by the following formula:

[0068] and r = r·r0, where represents the spatial gradient, r0 represents the relative position unit vector, r represents the position scalar, r represents the position vector, C T,+X , C T,-X They respectively represent the magnetic gradient tensor invariant of the plane corresponding to the positive direction of the X-axis and the magnetic gradient tensor invariant of the plane corresponding to the negative direction of the X-axis, i is the direction vector of the X-axis, and d is the preset baseline length.

[0069] Based on the position vector and the body center magnetic gradient tensor, the candidate value of the positioning parameter is obtained, wherein the candidate value of the positioning parameter is calculated by the following formula: firstly, the position vector r, the position scalar r and its position vector components r1, r2 and r3, i.e., the corresponding r x 、r y and r z Substitute the following formula:

[0070] In the formula, i and j are 1, 2, and 3 respectively. When i=j, δ ij =1; when i≠j, δ ij =0, simplifying to get:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Where μ is the magnetic permeability, in air μ≈4π×10 -7 Tm / A. Because the position vector r and its r1, r2, r3 are known, and G 11 , G 12 , G 13 , G 23 , G 33 It is also known that only m1, m2, m3 and the magnetic moment vector M are unknown. The magnetic moment vector M and its components are obtained by solving the five equations. 11 , G 12 , G 13 , G 23 , G 33 are the five independent components of the body-centered magnetic gradient tensor B xx , B xy , B xz , B zy , B zz , obtained by differential processing of the magnetic induction intensity measured by the sensor array.

[0077] The embodiment of the present application obtains the magnetic induction intensity through the sensor array, and then obtains the face-center magnetic gradient tensor through differential processing, obtains the magnetic gradient tensor invariant based on the face-center magnetic gradient tensor, obtains the spatial gradient based on the magnetic gradient tensor invariant, obtains the position vector and the magnetic moment vector of the ferromagnetic target based on the spatial gradient, that is, obtains the initial positioning of the ferromagnetic target, and then obtains a more accurate positioning result based on the initial positioning, so as to improve the accuracy of ferromagnetic target positioning.

[0078] S106, creating a nonlinear equation group based on the body-center magnetic gradient tensor and the magnetic dipole model.

[0079] Among them, the nonlinear equation group represents the mapping relationship between the magnetic gradient tensor difference and the positioning parameters, and the magnetic gradient tensor difference represents the difference between the measured magnetic gradient tensor and the theoretical magnetic gradient tensor.

[0080] When the distance between the ferromagnetic target and the measuring point is greater than or equal to 2.5 times the size of the ferromagnetic target, the ferromagnetic target can be equivalent to a magnetic dipole model. For example, if the radius of a magnetic iron ball is a meter and the distance from the iron ball to the center of the sensor array is 5a meters, the iron ball can be equivalent to a magnetic dipole model. Under the magnetic dipole model, the magnetic induction intensity at a distance r from the ferromagnetic target can be expressed as:

[0081] Among them, B represents the magnetic induction intensity, μ is the magnetic permeability, M is the magnetic moment vector, r is the position vector, and r is the position scalar.

[0082] The embodiment of the present application creates a nonlinear set of equations by treating the ferromagnetic target as a magnetic dipole model and based on the body-centered magnetic gradient tensor and the magnetic dipole model. The embodiment of the present application will describe the process of creating the nonlinear set of equations.

[0083] Specifically, as an optional implementation, the above S106 includes: calculating the magnetic induction intensity of each sensor in the sensor array according to the magnetic dipole model and performing differential processing to obtain a theoretical magnetic gradient tensor of the sensor array, where the theoretical magnetic gradient tensor includes positioning parameters;

[0084] Based on the difference between the body-centered magnetic gradient tensor and the theoretical magnetic gradient tensor, a set of nonlinear equations is established.

[0085] In the embodiment of the present application, the sensor is composed of multiple sensors. The magnetic induction intensity of each sensor and its magnetic induction intensity components in the directions of each coordinate axis are expressed according to the magnetic dipole model, and then the magnetic gradient tensor components corresponding to the body center of the sensor array are expressed by the above-mentioned differential processing method. Based on the five independent components, the body center magnetic gradient tensor containing position parameters, i.e., the theoretical magnetic gradient tensor, is obtained.

[0086] For example: Combine Figure 2 In the sensor array model shown in , taking sensor S1 as an example, the magnetic induction intensity and its magnetic induction intensity components measured by S1 are:

[0087]

[0088] and Among them, i, j and k represent the direction vectors of the X, Y and Z axes in the spatial coordinate system respectively. The other sensors are similar. Then, the above-mentioned differential processing method is used to express the various components of the body-centered magnetic gradient tensor of the sensor array, and the theoretical magnetic gradient tensor including the position parameters is obtained.

[0089] The body-center magnetic gradient tensor of the sensor array calculated according to the magnetic induction intensity actually measured by the sensor array is used as the measured magnetic gradient tensor. The measured magnetic gradient tensor and the theoretical magnetic intensity tensor are subtracted to establish a nonlinear equation group, which is:

[0090] Among them, F(m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, and the magnetic gradient tensor component B xx , B xy , B xz , B zy and B zz The matrix composed of represents the body-center magnetic gradient tensor calculated based on the actual measured magnetic induction intensity, that is, the measured magnetic gradient tensor, the magnetic gradient tensor component C xx , C xy , C xz , C zy , C zz The matrix composed of the theoretical magnetic gradient tensor, m x ,m y ,m z ,r x ,r y ,r z Represents a positional parameter.

[0091] The embodiment of the present application constructs a set of nonlinear equations by using the body-center magnetic gradient tensor obtained based on actual measurement data, that is, the difference between the measured magnetic gradient tensor and the body-center magnetic gradient tensor calculated based on the magnetic dipole model, that is, the theoretical magnetic gradient tensor, to facilitate the subsequent acquisition of the position of the ferromagnetic target based on the target value of the positioning parameter, thereby making the positioning result more accurate.

[0092] It should be understood that there is an error between the magnetic gradient tensor data calculated by the method of replacing differentials with differentials and the actual magnetic gradient tensor data according to the differential processing of the magnetic induction intensity measured by the sensor array and the actual magnetic gradient tensor data. In the embodiment of the present application, the magnetic induction intensity measured by the sensor array is differentially processed, and the obtained body-center magnetic gradient tensor is used as the measured magnetic gradient tensor. Then, according to the magnetic dipole model, the body-center magnetic gradient tensor is calculated and used as the theoretical magnetic gradient tensor. Based on the measured magnetic gradient tensor and the theoretical magnetic gradient tensor, a nonlinear set of equations is established, which can avoid the error introduced by replacing differentials with differentials, and then the position of the ferromagnetic target can be accurately determined based on the nonlinear set of equations in the subsequent process.

[0093] S108, taking the value of the magnetic gradient tensor difference satisfying the preset requirement as the optimization target, and correcting the candidate values ​​of the positioning parameters based on the preset optimization algorithm and the nonlinear equation group to obtain the target value of the positioning parameters.

[0094] Among them, the preset optimization algorithm is used to correct the candidate values ​​of the positioning parameters. Common numerical optimization algorithms include gradient descent method (Gradient Descent), trust region method (Trust Region Methods), etc. As an example, the present application adopts Levenberg-Marquardt (LM) algorithm to correct the candidate values ​​of the positioning parameters. The LM algorithm is an iterative optimization algorithm based on the trust region, which solves the problem of Hessian matrix non-positive definiteness, has a fast iteration speed, and is widely used in solving nonlinear optimization problems. The candidate values ​​of the positioning parameters are corrected by the preset optimization algorithm, so that after the corrected candidate values ​​are brought into the nonlinear equations, the calculated candidate values ​​of the magnetic gradient tensor difference meet the preset requirements, and the corrected candidate values ​​are determined as the target values ​​of the positioning parameters.

[0095] Specifically, as an optional implementation, the above S108 includes: calculating candidate values ​​of the magnetic gradient tensor difference based on candidate values ​​of the positioning parameters and a nonlinear equation group;

[0096] If the candidate value of the magnetic gradient tensor difference does not meet the preset requirements, the candidate value of the positioning parameter is corrected based on the preset optimization algorithm, and the steps of calculating the candidate value of the magnetic gradient tensor difference to correcting the candidate value of the positioning parameter based on the preset optimization algorithm are repeated based on the corrected candidate value until the candidate value of the magnetic gradient tensor difference meets the preset requirements;

[0097] If the value of the magnetic gradient tensor difference meets the preset requirement, the candidate value of the positioning parameter is used as the target value of the positioning parameter.

[0098] When the LM algorithm is used to correct the candidate value of the positioning parameter, the following formula may be used to correct the candidate value of the positioning parameter: Among them, x k represents the candidate value of the positioning parameter, x k+1 The modified candidate value is shown in the table, k represents the number of iterations, J k is the Jacobian matrix of the nonlinear system of equations, u k is the iteration parameter, represents the transpose of the Jacobian matrix, I is the identity matrix, F k Indicates based on x k The candidate value of the magnetic gradient tensor difference is calculated.

[0099] In the embodiment of the present application, the preset requirement includes that the difference between the current cumulative calculation result corresponding to the magnetic gradient tensor difference and the historical cumulative calculation result is less than the preset difference, and the current cumulative calculation result is calculated by the candidate value of the magnetic gradient tensor difference through the following formula:

[0100] f=min∑[F(m x ,m y ,m z ,r x ,r y ,r z ) 2 ], where f represents the current cumulative calculation result, F(m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, m x ,m y ,m z ,r x ,r y ,r z Represents a positional parameter.

[0101] It should be understood that the magnetic gradient tensor difference corresponds to five components, as shown by the following formula expansion:

[0102]

[0103] The current accumulated calculation result is the candidate value of the magnetic gradient tensor difference corresponding to the sum of the squares of five components.

[0104] Here, an example is used to illustrate the above correction process, which should not be understood as limiting the implementation method of the present application. Example 1: Based on the face-centre magnetic gradient tensor, a single-point positioning process is performed to obtain a candidate value of the positioning parameter A, and A is substituted into the nonlinear equation group to calculate the candidate value A' of the magnetic gradient tensor difference (A' includes five components A'1, A'2, A'3, A'4, A'5), and the current cumulative calculation result A' of the candidate value A' of the magnetic gradient tensor difference is calculated. 2 (i.e. A' 2 =A'1 2 +A'2 2 +A'3 2 +A'4 2 +A'5 2 );

[0105] The LM algorithm is used to correct the candidate value A of the positioning parameter, and the corrected candidate value B is brought into the nonlinear equation group to calculate the candidate value B' of the magnetic gradient tensor difference (B' includes five components B'1, B'2, B'3, B'4, B'5), and the current cumulative calculation result B' is calculated for B' 2 (i.e. B' 2 =B'1 2 +B'2 2 +B'3 2 +B'4 2 +B'5 2 ), at this time, the current cumulative calculation result B' 2 With A' 2 (i.e. historical cumulative calculation results) to make a difference, if the difference is B' 2 -A' 2 If it is smaller than the preset difference, B is used as the target value of the positioning parameter;

[0106] If the difference B' 2 -A' 2 If the difference is greater than or equal to the preset difference, then continue to correct B, substitute the corrected candidate value C into the nonlinear equation group, calculate the candidate value C' of the magnetic gradient tensor difference, and calculate the current accumulated calculation result C' for C' 2 , the current cumulative calculation result C' 2 With B' 2 (i.e. historical cumulative calculation results) to make a difference, if the difference C' 2 -B' 2 If it is less than the preset difference, C is used as the target value of the positioning parameter;

[0107] If the difference C' 2 -B' 2 If it is greater than or equal to the preset difference, C will continue to be corrected until the difference between the current cumulative calculation result and the historical cumulative calculation result is less than the preset difference, and the target value of the positioning parameter is determined.

[0108] As an example, if n (n≥2) consecutive differences (ie, the difference between the current cumulative calculation result and the historical cumulative calculation result) are less than the preset difference, the correction is stopped and the candidate value after the last correction is used as the target value of the positioning parameter.

[0109] The embodiment of the present application continuously corrects the candidate values ​​of the positioning parameters through an optimization algorithm to further reduce the difference between the measured body-center magnetic gradient tensor and the theoretical magnetic gradient tensor, thereby reducing the positioning error and meeting the accuracy requirements for positioning ferromagnetic targets in practical applications.

[0110] S110, determining an unbiased positioning result of the ferromagnetic target based on the target value of the positioning parameter.

[0111] The target value of the positioning parameter, that is, the specific position vector r, is converted into specific coordinates (x, y, z) to obtain an unbiased positioning result of the ferromagnetic target. The deviation between the position of the ferromagnetic target obtained by the method of the embodiment of the present application and the actual position of the ferromagnetic target is not much different.

[0112] In order to improve the accuracy of ferromagnetic target positioning, the magnetic induction intensity generated by the ferromagnetic target at the measuring point is obtained through the sensor array, and the face-center magnetic gradient tensor and body-center magnetic gradient tensor of the sensor array are determined based on the magnetic induction intensity. Single-point positioning processing is performed based on the face-center magnetic gradient tensor to obtain candidate values ​​of positioning parameters. Based on the body-center magnetic gradient tensor and the magnetic dipole model, a nonlinear equation group is created. The nonlinear equation group represents the mapping relationship between the magnetic gradient tensor difference and the positioning parameters. The candidate value of the magnetic gradient tensor difference that meets the preset requirements is taken as the optimization target. Based on the preset optimization algorithm and the nonlinear equation group, the candidate value of the positioning parameter is corrected to obtain the target value of the positioning parameter. Based on the target value of the positioning parameter, the unbiased positioning result of the ferromagnetic target is determined, which can reduce the positioning error and improve the positioning accuracy.

[0113] As another optional implementation, multiple measuring points can be set, and the multiple measuring points are equidistantly distributed on the same straight line. The sensor array measures the magnetic induction intensity generated by the ferromagnetic target at each measuring point, and the candidate value of the positioning parameter corresponding to each measuring point is calculated based on the magnetic induction intensity corresponding to each measuring point. The candidate values ​​of multiple positioning parameters are averaged, and the average value is corrected by a preset optimization algorithm. After the corrected average value is brought into the nonlinear equation group, the calculated candidate value of the magnetic gradient tensor difference meets the preset requirements, and the corrected average value is determined as the target value of the positioning parameter. In addition, when multiple measuring points are set, when constructing a nonlinear equation group, the corresponding body-center magnetic gradient tensor should be calculated based on the magnetic induction intensity at each measuring point, and multiple body-center magnetic gradient tensors are used as measured magnetic gradient tensors and are subtracted from the theoretical magnetic gradient tensor to establish a nonlinear equation group.

[0114] Specifically, in the case of multiple measuring points, assuming that the number of measuring points is n (n ≥ 2), the magnetic induction intensity measured by the sensor array is subjected to differential processing to obtain the body-centered magnetic gradient tensor of n measuring points and serve as the measured magnetic gradient tensor, as shown in the following formula: G = [G1 G2 G3 … G i … G n ] T , where G represents the measured magnetic gradient tensor, T is the transposed sign, G i =[B xx,i B xy,i B xz,i B zy,i B zz,i ]T represents the body-center magnetic gradient tensor calculated from the magnetic induction intensity measured by the sensor array at the i-th measuring point. The body-center magnetic gradient tensor is represented by B xx,i , B xy,i , B xz,i , B zy,i and B zz,i It consists of five magnetic gradient tensor components;

[0115] Accordingly, according to the magnetic dipole model, the body-center magnetic gradient tensor of n measuring points is calculated and used as the theoretical magnetic gradient tensor, as shown in the following formula: represents the theoretical magnetic gradient tensor, T is the transposed symbol, represents the body-centered magnetic gradient tensor calculated according to the magnetic dipole model at the i-th measuring point. The body-centered magnetic gradient tensor is given by and It consists of five magnetic gradient tensor components;

[0116] Furthermore, according to the measured magnetic gradient tensor and the theoretical magnetic gradient tensor, a nonlinear equation group is established, as shown in the following formula: Where F i (m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, G represents the measured magnetic gradient tensor, represents the theoretical magnetic gradient tensor, m x ,m y ,m z ,r x ,r y ,r z Indicates positioning parameters;

[0117] It should be understood that After expansion, it is shown as the following formula:

[0118]

[0119] In the formula represents the magnetic gradient tensor difference of the i-th measuring point, which is expanded as shown in the following formula:

[0120]

[0121] Accordingly, the current cumulative calculation result is obtained by calculating the candidate value of the magnetic gradient tensor difference through the following formula:

[0122] Among them, f represents the current cumulative calculation result, F i (m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference at the i-th measuring point, m x ,m y ,m z ,r x ,r y ,r z Represents a positional parameter.

[0123] It should be understood that the distance between multiple measuring points should not be too large. As an example, a measurement can be performed every 1 mm. Of course, it can also be reasonably set according to actual conditions, and this application does not make specific limitations on this.

[0124] The following is an explanation of the ferromagnetic target positioning method provided in the embodiment of the present application using a specific application scenario, which should not be construed as limiting the method of the embodiment of the present application. Figure 2 , which is a schematic diagram of the application scenario structure of a ferromagnetic target positioning method provided in an embodiment of the present application, and the application scenario includes: a sensor array, a slide rail, a signal collector, a host computer, an iron ball, etc.

[0125] Among them, the sensor array is composed of 8 three-axis fluxgate sensors, which are distributed in a cube, and the array baseline length is 0.33m; the sensor array is placed on a slide rail and can be moved to measure the magnetic induction intensity of multiple measuring points; the ruler on the slide rail is used to record the distance moved by the sensor array, and the spacing between black and white stripes is 1mm; the signal collector is used to collect the magnetic induction intensity signal obtained by the sensor array and condition, convert, cache and process the collected signal. The processed magnetic induction intensity signal is displayed by the host computer to facilitate related analysis and calculation; the iron ball is a ferromagnetic target. It should be understood that the slide rail is not necessary, and other possible measures can also be used to move the sensor array and measure the magnetic induction intensity at different measuring points. The embodiment of the present application does not limit this.

[0126] An iron ball is pre-placed on one side of the sensor array and the slide rail. The diameter of the iron ball is 40 cm, and the shortest distance between the iron ball and the sensor array is greater than 1 m.

[0127] First, the sensor array moves on the slide rail, and the measured magnetic induction intensity is recorded every 1 mm of movement. For each measurement, the magnetic induction intensity measured by the 8 sensors of the sensor array and its magnetic induction intensity components in the X, Y and Z axis directions are differentially processed to obtain the body-centered magnetic gradient tensor and the face-centered magnetic gradient tensor of the sensor array, and then the body-centered magnetic gradient tensor and the face-centered magnetic gradient tensor of the sensor array at multiple measuring points are obtained;

[0128] The iron ball is equivalent to a magnetic dipole model. The magnetic induction intensity and its components of the eight sensors of the sensor array are calculated through the magnetic induction intensity expression of the magnetic dipole model and differential processing is performed. The body-center magnetic gradient tensor of the sensor array is calculated and used as the theoretical magnetic gradient tensor.

[0129] The body-centre magnetic gradient tensor of the sensor array at multiple measuring points is used as the measured magnetic gradient tensor and is subtracted from the theoretical magnetic gradient tensor to establish a nonlinear equation;

[0130] Based on the surface center magnetic gradient tensor of the sensor array at each measuring point, the candidate values ​​of the positioning parameters corresponding to each measuring point are obtained (i.e., the corresponding Figure 3 The average value (i.e. the corresponding initial position) is calculated by averaging the candidate values ​​of the positioning parameters corresponding to multiple measuring points. Figure 3 The average value of the average position shown in the figure is corrected so that after the corrected average value is brought into the nonlinear equation group, the candidate value of the magnetic gradient tensor difference calculated meets the preset requirements, and the corrected average value is determined as the target value of the positioning parameter (i.e., the corresponding Figure 3 The final position shown in ), the position of the iron ball is obtained.

[0131] Finally, the final position of the iron ball obtained in the above steps is compared with the actual position of the iron ball to calculate the positioning error. Experiments have shown that when the number of measuring points is greater than or equal to 4, the calculated positioning error is less than 0.1. Compared with the current positioning technology, the positioning accuracy is greatly improved. At the same time, the iron ball is placed on the surface (i.e. Figure 2 1) and underground (i.e. Figure 2 In the position shown in 2), the iron ball can be accurately positioned both on the surface and underground. In addition, in order to facilitate readers to clearly understand the effect that can be achieved by positioning in this application, Figure 3 A schematic diagram showing the positioning results of a ferromagnetic target provided by an embodiment of the present application is shown in FIG. Figure 3 As shown, the initial positions corresponding to the multiple measuring points, the average positions of the multiple measuring points and the final positions obtained by the method of the present application are shown. Figure 3 The data shown are experimental data and are for readers' reference only, and should not be construed as limiting the methods of the embodiments of the present application.

[0132] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] With the above Figure 4 Corresponding to the ferromagnetic target positioning method shown, the present application embodiment also provides a ferromagnetic target positioning device. Figure 4 , is a structural schematic diagram of a ferromagnetic target positioning device 400 provided in one embodiment of the present application, the device 400 includes: an acquisition unit 410, a processing unit 420, a creation unit 430, a correction unit 440 and a determination unit 450.

[0134] The acquisition unit 410 is used to acquire the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determine the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity.

[0135] The processing unit 420 is used to perform single-point positioning processing based on the face center magnetic gradient tensor to obtain candidate values ​​of positioning parameters.

[0136] The creation unit 430 is used to create a nonlinear equation group based on the body-centered magnetic gradient tensor and the magnetic dipole model, wherein the nonlinear equation group represents a mapping relationship between the magnetic gradient tensor difference and the positioning parameters.

[0137] The correction unit 440 is used to take the candidate value of the magnetic gradient tensor difference satisfying the preset requirement as the optimization target, and based on the preset optimization algorithm and the nonlinear equation group, correct the candidate value of the positioning parameter to obtain the target value of the positioning parameter.

[0138] The determination unit 450 is used to determine the unbiased positioning result of the ferromagnetic target based on the target value of the positioning parameter.

[0139] Optionally, when the sensor array is composed of a plurality of sensors and the plurality of sensors are distributed in a cubic shape, the acquisition unit 410 performs the following steps when determining the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity: determining the magnetic induction intensity components of the magnetic induction intensity measured by each sensor in the directions of each coordinate axis in a target three-dimensional coordinate system, wherein the target three-dimensional coordinate system is a three-dimensional coordinate system established with the body center as the origin, and the body center refers to the center of the cube formed by the arrangement of the plurality of sensors;

[0140] Performing differential processing based on the magnetic induction intensity components to obtain a plurality of magnetic gradient tensor components at the body center and a plurality of magnetic gradient tensor components corresponding to each of a plurality of surface centers, wherein the surface center refers to the center of the plane where the plurality of sensors are located;

[0141] Arranging a plurality of magnetic gradient tensor components of the body center to obtain the body center magnetic gradient tensor;

[0142] The plurality of magnetic gradient tensor components corresponding to the plurality of surface centers are arranged to obtain the surface center magnetic gradient tensors corresponding to the plurality of surface centers.

[0143] Optionally, when performing differential processing based on the magnetic induction intensity components to obtain multiple magnetic gradient tensor components of the body center and multiple magnetic gradient tensor components corresponding to each of the multiple face centers, the acquisition unit 410 performs the following steps: calculating a first magnetic induction intensity mean value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane on the same coordinate axis, and a second magnetic induction intensity mean value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane and adjacent to each other on the same coordinate axis;

[0144] Based on the ratio between the first magnetic induction intensity mean value and a preset baseline length, a plurality of magnetic gradient tensor components of the body center are obtained;

[0145] Based on the ratio between the second magnetic induction intensity average and the preset baseline length, a plurality of magnetic gradient tensor components of the surface center are obtained.

[0146] Optionally, when the processing unit 420 performs the single-point positioning processing based on the face-center magnetic gradient tensor to obtain the candidate value of the positioning parameter, the processing unit 420 performs the following steps: for each face-center magnetic gradient tensor, based on the sum of squares of multiple face-center magnetic gradient tensor components corresponding to the face-center magnetic gradient tensor, obtain a magnetic gradient tensor invariant corresponding to the face-center magnetic gradient tensor;

[0147] Calculating the difference between multiple magnetic gradient tensor invariants corresponding to multiple face-center magnetic gradient tensors;

[0148] Based on the ratio between the difference and a preset baseline length, a spatial gradient is obtained;

[0149] Normalizing the spatial gradient to obtain a position vector of the ferromagnetic target;

[0150] Based on the position vector and the body-centre magnetic gradient tensor, candidate values ​​of the positioning parameters are obtained.

[0151] Optionally, the magnetic gradient tensor invariant is calculated by the following formula:

[0152] Among them, C T represents the magnetic gradient tensor invariant, G ij represents the face-centre magnetic gradient tensor component;

[0153] The position vector is calculated by the following formula: and r = r·r0, where represents the spatial gradient, r0 represents the relative position unit vector, r represents the position scalar, and r represents the position vector.

[0154] Optionally, when creating the nonlinear equation group based on the body-center magnetic gradient tensor and the magnetic dipole model, the creation unit 430 performs the following steps: calculating the magnetic induction intensity of each sensor in the sensor array according to the magnetic dipole model and performing differential processing to obtain a theoretical magnetic gradient tensor of the sensor array, wherein the theoretical magnetic gradient tensor includes the positioning parameter;

[0155] The nonlinear equation group is established based on the difference between the body-center magnetic gradient tensor and the theoretical magnetic gradient tensor.

[0156] Optionally, the nonlinear equations are:

[0157] Among them, F(m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, the magnetic gradient tensor component B xx , B xy , B xz , B zy and B zz The matrix composed of represents the body-centered magnetic gradient tensor, the magnetic gradient tensor component C xx , C xy , C xz , C zy , C zz The matrix composed of the theoretical magnetic gradient tensor, m x ,m y ,m z ,r x ,r y ,r z Represents the positioning parameters.

[0158] Optionally, the correction unit 440 performs the following steps when taking the candidate value of the magnetic gradient tensor difference satisfying a preset requirement as the optimization target and correcting the candidate value of the positioning parameter based on a preset optimization algorithm and the nonlinear equation group to obtain the target value of the positioning parameter: calculating the candidate value of the magnetic gradient tensor difference based on the candidate value of the positioning parameter and the nonlinear equation group;

[0159] If the candidate value of the magnetic gradient tensor difference does not meet the preset requirement, the candidate value of the positioning parameter is corrected based on the preset optimization algorithm, and the steps of calculating the candidate value of the magnetic gradient tensor difference to correcting the candidate value of the positioning parameter based on the preset optimization algorithm are repeated based on the corrected candidate value until the candidate value of the magnetic gradient tensor difference meets the preset requirement;

[0160] If the value of the magnetic gradient tensor difference meets the preset requirement, the candidate value of the positioning parameter is used as the target value of the positioning parameter.

[0161] Optionally, the candidate value of the positioning parameter is corrected using the following formula:

[0162] Among them, x k represents the candidate value of the positioning parameter, x k+1 The modified candidate value is shown in the table, k represents the number of iterations, J k is the Jacobian matrix of the nonlinear system of equations, u k is the iteration parameter, represents the transpose of the Jacobian matrix, I is the identity matrix, F k Indicates based on x k a calculated candidate value of the magnetic gradient tensor difference;

[0163] The preset requirement includes that the difference between the current cumulative calculation result and the historical cumulative calculation result corresponding to the magnetic gradient tensor difference is less than the preset difference, and the current cumulative calculation result is calculated by the candidate value of the magnetic gradient tensor difference through the following formula:

[0164] f=min∑[F(m x ,m y ,m z ,r x ,r y ,r z ) 2 ], where f represents the current cumulative calculation result, F(m x ,m y ,m z ,r x ,r y ,rz ) represents the magnetic gradient tensor difference, m x ,m y ,m z ,r x ,r y ,r z Represents the positioning parameters.

[0165] Obviously, the ferromagnetic target positioning device provided in the embodiment of the present application can be used as Figure 1 The execution subject of the ferromagnetic target positioning method shown, for example Figure 1 In the ferromagnetic target positioning method shown in FIG. 1 , step S102 may be performed by Figure 4 The acquisition unit 410 in the ferromagnetic target positioning device shown in FIG. 1 is executed, and step S104 can be performed by Figure 4 The processing unit 420 in the ferromagnetic target positioning device shown in FIG. 1 is executed, and step S106 can be performed by Figure 4 The creation unit 430 in the ferromagnetic target positioning device shown in FIG. 1 is executed, and step S108 can be performed by Figure 4 The correction unit 440 in the ferromagnetic target positioning device shown in FIG. 1 is executed, and step S110 can be performed by Figure 4 The determination unit 450 in the ferromagnetic target locating device shown is executed.

[0166] According to another embodiment of the present application, Figure 4 The various units in the ferromagnetic target positioning device shown can be separately or completely combined into one or several other units to constitute, or one (some) of the units can also be split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the ferromagnetic target positioning device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0167] According to another embodiment of the present application, the program can be executed on a general computing device such as a computer including a central processing unit (CPU), a random access memory (RAM), a read-only memory (ROM), and other processing elements and storage elements. Figure 1 A computer program (including program code) for each step involved in the corresponding method shown in FIG. Figure 4The ferromagnetic target positioning device shown in the embodiment of the present application is implemented to realize the ferromagnetic target positioning method of the embodiment of the present application. The computer program can be recorded on a computer-readable storage medium, for example, and transferred to an electronic device through the computer-readable storage medium and run therein.

[0168] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0169] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0170] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0171] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a data processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0172] Acquire the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determine the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity;

[0173] Performing single-point positioning processing based on the face-centre magnetic gradient tensor to obtain candidate values ​​of positioning parameters;

[0174] Based on the body-centered magnetic gradient tensor and the magnetic dipole model, a nonlinear equation group is created, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters;

[0175] Taking the candidate value of the magnetic gradient tensor difference satisfying the preset requirement as the optimization target, based on the preset optimization algorithm and the nonlinear equation group, the candidate value of the positioning parameter is corrected to obtain the target value of the positioning parameter;

[0176] Based on the target value of the positioning parameter, an unbiased positioning result of the ferromagnetic target is determined.

[0177] The above application Figure 4 The method performed by the ferromagnetic target positioning device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0178] The electronic device may also perform Figure 1 Method and implementation of ferromagnetic target positioning device in Figure 1 , Figure 4 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0179] Of course, in addition to software implementation methods, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0180] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 The method of the embodiment shown is specifically used to perform the following operations:

[0181] Acquire the magnetic induction intensity generated by the ferromagnetic target at the measuring point through the sensor array, and determine the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity;

[0182] Performing single-point positioning processing based on the face-centre magnetic gradient tensor to obtain candidate values ​​of positioning parameters;

[0183] Based on the body-centered magnetic gradient tensor and the magnetic dipole model, a nonlinear equation group is created, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters;

[0184] Taking the candidate value of the magnetic gradient tensor difference satisfying the preset requirement as the optimization target, based on the preset optimization algorithm and the nonlinear equation group, the candidate value of the positioning parameter is corrected to obtain the target value of the positioning parameter;

[0185] Based on the target value of the positioning parameter, an unbiased positioning result of the ferromagnetic target is determined.

[0186] In short, 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. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0187] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0188] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0189] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0190] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A ferromagnetic target positioning method, characterized in that: include: The magnetic induction intensity generated by the ferromagnetic target at the measuring point is obtained through a sensor array, and based on the magnetic induction intensity, a face-center magnetic gradient tensor and a body-center magnetic gradient tensor of the sensor array are determined, wherein the sensor array is composed of a plurality of sensors, and the plurality of sensors are distributed in a cubic shape, the face-center magnetic gradient tensor is a magnetic gradient tensor at a face center, and the face center refers to the center of a plane where the plurality of sensors are located, and the body-center magnetic gradient tensor is a magnetic gradient tensor at a body center, and the body center refers to the center of a cube formed by the arrangement of the plurality of sensors; For each face-center magnetic gradient tensor, based on the square sum of multiple face-center magnetic gradient tensor components corresponding to the face-center magnetic gradient tensor, a magnetic gradient tensor invariant corresponding to the face-center magnetic gradient tensor is obtained; the difference between multiple magnetic gradient tensor invariants corresponding to multiple face-center magnetic gradient tensors is calculated; based on the ratio between the difference and a preset baseline length, a spatial gradient is obtained; the spatial gradient is normalized to obtain a position vector of the ferromagnetic target; based on the position vector and the body-center magnetic gradient tensor, a candidate value of the positioning parameter is obtained; Based on the body-center magnetic gradient tensor and the magnetic dipole model, a nonlinear equation group is created, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters, wherein the magnetic gradient tensor difference is a difference between a body-center magnetic gradient tensor and a theoretical magnetic gradient tensor, wherein the theoretical magnetic gradient tensor is a body-center magnetic gradient tensor of the sensor array determined based on the magnetic induction intensities of a plurality of sensors of the sensor array characterized by the magnetic dipole model; Taking the candidate value of the magnetic gradient tensor difference satisfying the preset requirement as the optimization target, based on the preset optimization algorithm and the nonlinear equation group, the candidate value of the positioning parameter is corrected to obtain the target value of the positioning parameter, wherein the method for determining the target value of the positioning parameter includes: correcting the candidate value of the positioning parameter by the preset optimization algorithm, so that after the corrected candidate value is brought into the nonlinear equation group, the calculated candidate value of the magnetic gradient tensor difference satisfies the preset requirement, and then determining the corrected candidate value as the target value of the positioning parameter; Based on the target value of the positioning parameter, an unbiased positioning result of the ferromagnetic target is determined.

2. The method according to claim 1, characterized in that: The determining, based on the magnetic induction intensity, a face-centered magnetic gradient tensor and a body-centered magnetic gradient tensor of the sensor array comprises: Determine the magnetic induction intensity components of the magnetic induction intensity measured by each sensor in the directions of each coordinate axis in the target three-dimensional coordinate system, wherein the target three-dimensional coordinate system is a three-dimensional coordinate system established with the body center as the origin; Performing differential processing based on the magnetic induction intensity components to obtain a plurality of magnetic gradient tensor components at the body center and a plurality of magnetic gradient tensor components corresponding to each of the plurality of face centers; Arranging a plurality of magnetic gradient tensor components of the body center to obtain the body center magnetic gradient tensor; The plurality of magnetic gradient tensor components corresponding to the plurality of surface centers are arranged to obtain the surface center magnetic gradient tensors corresponding to the plurality of surface centers.

3. The method according to claim 2, characterized in that The differential processing based on the magnetic induction intensity component is performed to obtain a plurality of magnetic gradient tensor components at the body center and a plurality of magnetic gradient tensor components corresponding to each of the plurality of face centers, including: Calculate a first magnetic induction intensity average value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane on the same coordinate axis, and a second magnetic induction intensity average value corresponding to an average value of magnetic induction intensity components of multiple sensors located in the same plane and adjacent to each other on the same coordinate axis; Based on the ratio between the first magnetic induction intensity mean value and a preset baseline length, a plurality of magnetic gradient tensor components of the body center are obtained; Based on the ratio between the second magnetic induction intensity average and the preset baseline length, a plurality of magnetic gradient tensor components of the surface center are obtained.

4. The method according to claim 1, characterized in that The magnetic gradient tensor invariant is calculated by the following formula: Among them, C T represents the magnetic gradient tensor invariant, G ij represents the face-centre magnetic gradient tensor component; The position vector is calculated by the following formula: and r = r·r0, where represents the spatial gradient, r0 represents the relative position unit vector, r represents the position scalar, and r represents the position vector.

5. The method according to claim 1, characterized in that: The method of creating a nonlinear set of equations based on the body-centered magnetic gradient tensor and the magnetic dipole model comprises: Calculating the magnetic induction intensity of each sensor in the sensor array according to the magnetic dipole model and performing differential processing to obtain a theoretical magnetic gradient tensor of the sensor array, wherein the theoretical magnetic gradient tensor includes the positioning parameter; The nonlinear equation group is established based on the difference between the body-center magnetic gradient tensor and the theoretical magnetic gradient tensor.

6. The method according to claim 5, characterized in that The nonlinear equations are: Among them, F(m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, the magnetic gradient tensor component B xx , B xy , B xz , B zy and B zz The matrix composed of represents the body-centered magnetic gradient tensor, the magnetic gradient tensor component C xx , C xy , C xz , C zy , C zz The matrix composed of the theoretical magnetic gradient tensor, m x ,m y ,m z ,r x ,r y ,r z Represents the positioning parameters.

7. The method according to claim 1, characterized in that The step of making the candidate value of the magnetic gradient tensor difference satisfy a preset requirement as an optimization target, and correcting the candidate value of the positioning parameter based on a preset optimization algorithm and the nonlinear equation group to obtain the target value of the positioning parameter includes: Calculating candidate values ​​of the magnetic gradient tensor difference based on the candidate values ​​of the positioning parameters and the nonlinear equation set; If the candidate value of the magnetic gradient tensor difference does not meet the preset requirement, the candidate value of the positioning parameter is corrected based on the preset optimization algorithm, and the steps of calculating the candidate value of the magnetic gradient tensor difference to correcting the candidate value of the positioning parameter based on the preset optimization algorithm are repeated based on the corrected candidate value until the candidate value of the magnetic gradient tensor difference meets the preset requirement; If the value of the magnetic gradient tensor difference meets the preset requirement, the candidate value of the positioning parameter is used as the target value of the positioning parameter.

8. The method according to claim 7, characterized in that The candidate values ​​of the positioning parameters are corrected using the following formula: Among them, x k represents the candidate value of the positioning parameter, x k+1 The modified candidate value is shown in the table, k represents the number of iterations, J k is the Jacobian matrix of the nonlinear system of equations, u k is the iteration parameter, represents the transpose of the Jacobian matrix, I is the identity matrix, F k Indicates based on x k a calculated candidate value of the magnetic gradient tensor difference; The preset requirement includes that the difference between the current cumulative calculation result and the historical cumulative calculation result corresponding to the magnetic gradient tensor difference is less than the preset difference, and the current cumulative calculation result is calculated by the candidate value of the magnetic gradient tensor difference through the following formula: Wherein, f represents the current cumulative calculation result, F(m x ,m y ,m z ,r x ,r y ,r z ) represents the magnetic gradient tensor difference, m x ,m y ,m z ,r x ,r y ,r z Represents the positioning parameters.

9. A ferromagnetic target positioning device, characterized in that: include: an acquisition unit, configured to acquire the magnetic induction intensity generated by the ferromagnetic target at the measuring point through a sensor array, and determine the face-center magnetic gradient tensor and the body-center magnetic gradient tensor of the sensor array based on the magnetic induction intensity, wherein the sensor array is composed of a plurality of sensors, and the plurality of sensors are distributed in a cubic shape, the face-center magnetic gradient tensor is the magnetic gradient tensor at the face center, and the face center refers to the center of the plane where the plurality of sensors are located, and the body-center magnetic gradient tensor is the magnetic gradient tensor at the body center, and the body center refers to the center of the cube formed by the arrangement of the plurality of sensors; A processing unit is used to obtain, for each face-center magnetic gradient tensor, a magnetic gradient tensor invariant corresponding to the face-center magnetic gradient tensor based on the square sum of multiple face-center magnetic gradient tensor components corresponding to the face-center magnetic gradient tensor; calculate the difference between multiple magnetic gradient tensor invariants corresponding to multiple face-center magnetic gradient tensors; obtain a spatial gradient based on a ratio between the difference and a preset baseline length; perform unitization processing on the spatial gradient to obtain a position vector of the ferromagnetic target; and obtain a candidate value of the positioning parameter based on the position vector and the body-center magnetic gradient tensor; A creation unit, configured to create a nonlinear equation group based on the body-center magnetic gradient tensor and the magnetic dipole model, wherein the nonlinear equation group represents a mapping relationship between a magnetic gradient tensor difference and the positioning parameters, wherein the magnetic gradient tensor difference is a difference between a body-center magnetic gradient tensor and a theoretical magnetic gradient tensor, wherein the theoretical magnetic gradient tensor is a body-center magnetic gradient tensor of the sensor array determined based on magnetic induction intensities of a plurality of sensors of the sensor array characterized by the magnetic dipole model; A correction unit, used for taking the value of the magnetic gradient tensor difference satisfying a preset requirement as an optimization target, and correcting the candidate value of the positioning parameter based on a preset optimization algorithm and the nonlinear equation group to obtain a target value of the positioning parameter, wherein the target value of the positioning parameter is determined in a manner including: correcting the candidate value of the positioning parameter by using the preset optimization algorithm, so that after the corrected candidate value is brought into the nonlinear equation group, the calculated candidate value of the magnetic gradient tensor difference satisfies the preset requirement, and then determining the corrected candidate value as the target value of the positioning parameter; A determination unit is used to determine an unbiased positioning result of the ferromagnetic target based on a target value of the positioning parameter.

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