Single-target permanent magnet positioning method and device based on magnetic dipole model

Abstract

The invention provides a single-target permanent magnet positioning method and device based on a magnetic dipole model, and relates to the technical field of magnetic sensing, and the single-target permanent magnet positioning method based on the magnetic dipole model comprises the steps: generating a uniform magnetic field through a Helmholtz coil, and measuring the magnetic moment data of a to-be-measured magnetic dipole; the magnetic dipole to be tested is a cylindrical permanent magnet; the MSSA algorithm used for short-distance positioning and the ICNN-GRU-Attention neural network algorithm used for long-distance positioning are fused, and a fusion positioning algorithm is obtained; and inputting the magnetic moment data and magnetic field intensity data received by using a sensor array into the fusion positioning algorithm, and determining a target position of the magnetic dipole to be measured. According to the method, the positioning area is divided into the close-distance area, the mixed area and the long-distance area, and the positioning algorithms in different application ranges are fused to generate the adaptive fusion algorithm, so that the positioning precision of the single-target permanent magnet is improved.

Description

Technical Field

[0001] The invention relates to the field of magnetic sensing technology, and in particular to a single-target permanent magnet positioning method and device based on a magnetic dipole model. Background Art

[0002] Magnetic positioning technology has been widely used in high-precision real-time positioning and tracking systems in a small range due to its characteristic of not being restricted by line of sight. In complex surgical environments, surgical medical robots need to accurately locate the target position. Magnetic positioning technology can ensure that it is still accurate even in conditions of limited line of sight or insufficient light. Similarly, in terms of tumor and drug tracking, magnetic positioning technology can also achieve precise positioning, which provides technical support for the accuracy of tumor treatment and monitoring of drug efficacy. The widespread application of magnetic positioning technology has not only improved the efficiency and accuracy of medical services, but also provided strong technical support for the development of the medical field.

[0003] However, due to the insufficient performance of the magnetic positioning algorithm, the limitations of sensor sensitivity and range, and the influence of other external changing magnetic fields such as the geomagnetic field, the positioning accuracy is easily disturbed, thus failing to meet high accuracy requirements. How to adapt to different positioning ranges and improve positioning accuracy has become a problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the current magnetic positioning algorithm has insufficient performance and is limited by sensor sensitivity and range, as well as the influence of other external changing magnetic fields such as the geomagnetic field, resulting in insufficient positioning accuracy. The present invention provides a single-target permanent magnet positioning method and device based on a magnetic dipole model.

[0005] The technical solution of the embodiment of the present application is implemented as follows: A first aspect of an embodiment of the present application provides a single target permanent magnet positioning method based on a magnetic dipole model, comprising: A uniform magnetic field is generated by a Heimholtz coil to measure the magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet; The MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning are integrated to obtain a fusion positioning algorithm. The magnetic moment data and the magnetic field strength data received by the sensor array are input into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

[0006] Optionally, the method of using a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of a magnetic dipole to be measured includes: Determine the magnetic flux parameters of the magnetic dipole to be measured by using a Heimholtz coil and a fluxmeter; A mapping relationship between the magnetic flux parameter and the magnetic moment data is established by Ampere's circuit theorem or the superposition principle of magnetic fields to determine the magnetic moment data of the magnetic dipole to be measured.

[0007] Optionally, the method of fusing the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm further includes: The SSA sparrow algorithm is improved by using the mutated cat chaotic mapping, the improvement of the ratio coefficient between the discoverer and the joiner and the update of the discoverer position, and the MSSA algorithm for close-range positioning is obtained; specifically, The population is initialized using the mutated cat chaotic map. The ratio coefficient r of discoverers to joiners is updated once every generation. The update formula of r is as follows: ; in, is the initial scale factor set, is the current iteration number, is the maximum number of iterations set, is the disturbance factor set, is a random number in the interval [0,1]; The discoverer position update formula is improved as follows:

[0008] in, Represents the current iteration number, , represents the maximum number of iterations, Indicates A sparrow in the Location information in the dimension, is a random number, and Represent the warning value and safety value respectively. , , is a random number that follows a normal distribution. Indicates a A matrix where all elements are 1. , the finder can perform a wider search operation if , which means that the discoverer has discovered the predator, and all sparrows need to fly quickly to a safe place to find food.

[0009] Optionally, the method of fusing the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm further includes: The convolutional neural network, gated recurrent unit and attention mechanism are combined to obtain the ICNN-GRU-Attention neural network algorithm for long-distance positioning; the loss function of the ICNN-GRU-Attention neural network algorithm is the mean square error: ; in, for A matrix representing the position and posture information predicted by the network, Represents the real position and posture information .

[0010] Optionally, before inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured, the method further includes: Fixing the magnetic dipole to be measured by using a positioning plate; A plurality of magnetic sensors are formed into an array to obtain magnetic field intensity data of the magnetic dipole to be measured at different positions.

[0011] Optional, set The coordinates of the sensors are , , P is the distance from the center of the magnet to the The distance between sensors is represented by the vector The magnetic dipole to be measured is The magnetic field strength at each sensor location is: ; in, express The unit vector in the direction of the axis, the magnet The magnetic field strength at the sensor exist The orthogonal components in the axial direction are , the expression is as follows: ; in, is the modulus of P, .

[0012] Optionally, inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured includes: Determine the first target position of the magnetic dipole to be measured by using the MSSA algorithm; Determine the second target position of the magnetic dipole to be measured by using an ICNN-GRU-Attention neural network algorithm; The first target position and the second target position are processed in a weighted manner to determine the target position of the magnetic dipole to be measured.

[0013] A second aspect of the embodiment of the present application provides a single target permanent magnet positioning device based on a magnetic dipole model, comprising: a data measurement module, an algorithm fusion module and a position determination module, wherein: The data measurement module is configured to use a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet; The algorithm fusion module is configured to fuse the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm; The position determination module is configured to input the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

[0014] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the single-target permanent magnet positioning method based on the magnetic dipole model described in the first aspect.

[0015] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0016] Compared with the prior art, the technical solution provided by this application has the following beneficial effects: The present invention provides a single-target permanent magnet positioning method and device based on a magnetic dipole model, which uses a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of a magnetic dipole to be measured, and fuses the MSSA algorithm for close-range positioning and the ICNN-GRU-Attention neural network algorithm for long-range positioning to obtain a fusion positioning algorithm, thereby inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured. By dividing the positioning area into a close-range area, a mixed area and a long-range area, and fusing positioning algorithms with different applicable ranges to generate a matching fusion algorithm, the positioning accuracy of the single-target permanent magnet is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a single target permanent magnet positioning method based on a magnetic dipole model provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a single-target permanent magnet positioning device based on a magnetic dipole model provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] Below, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application. The terms used herein are only for describing specific embodiments and are not intended to limit the present application. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components. All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0019] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flow charts may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions, when executed by the processor, may create a device for implementing the functions / operations described in these block diagrams and / or flow charts.

[0020] In some embodiments, see Figure 1 , Figure 1 A schematic diagram of a flow chart of a single target permanent magnet positioning method based on a magnetic dipole model provided in an embodiment of the present application; a single target permanent magnet positioning method based on a magnetic dipole model provided in an embodiment of the present application includes: S110, using a Heimholtz coil to generate a uniform magnetic field to measure magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet.

[0021] The Helmholtz coil is a device consisting of two coaxial circular coils. When the currents in the two coils are equal in magnitude and direction, the magnetic field they generate is uniform in the center of the coil. The magnetic dipole to be measured is placed in the Helmholtz coil, the coil current is adjusted to generate magnetic fields of different strengths, and the force applied to the permanent magnet is recorded. Based on the relationship between the force applied and the magnetic field strength, as well as the physical parameters of the magnetic dipole to be measured (such as mass, volume, etc.), the magnetic moment of the magnetic dipole to be measured can be calculated.

[0022] In some embodiments, S110, using a Heimholtz coil to generate a uniform magnetic field to measure magnetic moment data of a magnetic dipole to be measured includes: Determine the flux parameters of the magnetic dipole to be measured using the Heimholtz coil and the fluxmeter; The mapping relationship between the magnetic flux parameter and the magnetic moment data is established through Ampere's circuit theorem or the superposition principle of the magnetic field, and the magnetic moment data of the magnetic dipole to be measured is determined.

[0023] In this embodiment, measuring the magnetic flux parameters and obtaining the magnetic moment data may include the following steps: 1) Setting the Helmholtz coil: Ensure that the two coils are coaxial and the current direction is the same, and adjust the current size to generate the required magnetic field strength. 2) Place the magnetic dipole to be measured: Place the magnetic dipole to be measured in the central area of ​​the Helmholtz coil to ensure that it is in a uniform magnetic field. 3) Measure the magnetic flux: Use a fluxmeter to measure the magnetic flux generated by the magnetic dipole to be measured in the Helmholtz coil. This magnetic flux will depend on the magnetic moment of the magnetic dipole and its position and direction relative to the coil. 4) Establish a mapping relationship. Taking the Ampere loop theorem as an example, the Ampere loop theorem shows that the line integral of the magnetic field intensity along a closed loop is equal to the sum of the currents enclosed by the loop multiplied by the magnetic permeability. However, when directly applied to magnetic dipoles, this theorem requires some conversion and approximation. Usually, the mathematical relationship between the magnetic flux parameters and the magnetic moment is established by using the magnetic field distribution formula generated by the magnetic dipole in space, combined with the idea of ​​the Ampere loop theorem. Taking the principle of magnetic field superposition as an example, the principle of magnetic field superposition points out that the total magnetic field in space is the vector sum of the magnetic fields generated by each magnetic source. For the case where the magnetic dipole to be measured is in the Helmholtz coil, the magnetic dipole can be regarded as a small magnetic source, and the magnetic field generated by it is superimposed with the magnetic field generated by the Helmholtz coil. By measuring the superimposed magnetic field (i.e., magnetic flux) and using the principle of magnetic field superposition and the magnetic dipole magnetic field distribution formula, the magnetic moment of the magnetic dipole can be derived. 5) Calculate the magnetic moment data. After establishing the mapping relationship between the magnetic flux parameters and the magnetic moment, the magnetic moment data of the magnetic dipole can be calculated by substituting the measured magnetic flux.

[0024] S120, the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning are fused to obtain a fused positioning algorithm.

[0025] Here, the MSSA algorithm is an improved version of the multi-objective sparrow search optimization algorithm. The ICNN-GRU-Attention neural network algorithm is an algorithm that combines Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and Attention Mechanism (Attention). The MSSA algorithm, a short-range area positioning algorithm, is combined with the ICNN-GRU-Attention neural network algorithm, a long-range area positioning algorithm, to form a full-area fusion positioning algorithm that can simultaneously expand the positioning area and improve positioning accuracy.

[0026] In some embodiments, S120, fusing the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm, further comprising: The SSA sparrow algorithm is improved by using the mutated cat chaotic mapping, the improvement of the ratio coefficient between the discoverer and the joiner and the update of the discoverer position, and the MSSA algorithm for close-range positioning is obtained; specifically, The population is initialized using the mutated cat chaotic map. The ratio coefficient r of discoverers to joiners is updated once every generation. The update formula of r is as follows: ; in, is the initial scale factor set, is the current iteration number, is the maximum number of iterations set, is the disturbance factor set, is a random number in the interval [0,1]; The discoverer position update formula is improved as follows:

[0027] in, Represents the current iteration number, , represents the maximum number of iterations, Indicates A sparrow in Location information in the dimension, is a random number, and Represent the warning value and safety value respectively. , , is a random number that follows a normal distribution. Indicates a A matrix where all elements are 1. , the finder can perform a wider search operation if , which means that the discoverer has discovered the predator, and all sparrows need to fly quickly to a safe place to find food.

[0028] In this embodiment, the variant cat mapping is a two-dimensional reversible chaotic mapping, and the vertical and horizontal chaotic sequences generated between [0, 1] are uniformly distributed. The population is initialized by the variant cat chaotic mapping method, which can generate a population uniformly distributed within the set range. The variant cat chaotic mapping requires that the more the population is set, the more uniform the overall population generated will be. The improved sparrow algorithm has a faster convergence speed while maintaining the original advantages. To achieve the same target value, the number of generations required for convergence can be reduced by about half, which improves the algorithm running speed. The positioning time is shortened from 0.068s to 0.034s. At the same time, it has higher stability, and the convergence is completed in an average of 70 times, and the convergence result error is less than one thousandth.

[0029] In some embodiments, S120, fusing the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm, further comprising: Combining convolutional neural networks, gated recurrent units, and attention mechanisms, we obtain the ICNN-GRU-Attention neural network algorithm for long-distance positioning. The loss function of the ICNN-GRU-Attention neural network algorithm is the mean square error: ; in, for A matrix representing the position and posture information predicted by the network, Represents the real position and posture information .

[0030] S130, inputting the magnetic moment data and the magnetic field strength data received by the sensor array into a fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

[0031] Here, the measured magnetic moment data and the magnetic field strength data received by the sensor array are input into a fusion positioning algorithm, and the algorithm outputs the position information of the permanent magnet, including the coordinates and possible error range, based on the input magnetic moment and magnetic field strength data.

[0032] In some embodiments, at S130, before inputting the magnetic moment data and the magnetic field strength data received by the sensor array into a fusion positioning algorithm to determine the target position of the magnetic dipole to be measured, the method further includes: The magnetic dipole to be measured is fixed by using a positioning plate; Multiple magnetic sensors are formed into an array to obtain magnetic field strength data of magnetic dipoles to be measured at different positions.

[0033] In this embodiment, a mathematical model of the magnetic field strength of a single cylindrical permanent magnet is constructed based on a magnetic dipole, and a permanent magnet positioning plate, a magnetic sensor array, and a host computer software based on MATLAB are designed. A flat plate that can stably fix the permanent magnet is designed in advance to ensure that the permanent magnet remains in position during the experiment. The material of the positioning plate is a non-magnetic material-acrylic material. A graphical user interface (GUI) based on MATLAB is developed to receive sensor data, process data, display a magnetic field distribution map, and calculate the position of the permanent magnet.

[0034] In some embodiments, The coordinates of the sensors are , , P is the distance from the center of the magnet to the The distance between sensors is represented by the vector The magnetic dipole to be measured is The magnetic field strength at each sensor location is: ; in, express The unit vector in the direction of the axis, the magnet The magnetic field strength at the sensor exist The orthogonal components in the axial direction are , the expression is as follows: ; in, is the modulus of P, .

[0035] In this embodiment, a global Cartesian coordinate system is set with the upper left corner of the positioning system sensor array as the origin. In the global coordinate system, there is a sensor array consisting of N sensors, and these sensors are all on the same horizontal plane. The sensor array is used to measure the magnetic field strength in space. Considering electromagnetic compatibility, signal integrity and layout rationality, in order to ensure that the sensor array can accurately and efficiently measure the magnetic field data, the magnetic sensor array is designed as a 3×3 nine-square grid structure.

[0036] In some embodiments, S130, inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured includes: Determine the first target position of the magnetic dipole to be measured by using the MSSA algorithm; The ICNN-GRU-Attention neural network algorithm is used to determine the second target position of the magnetic dipole to be measured; The first target position and the second target position are processed in a weighted manner to determine the target position of the magnetic dipole to be measured.

[0037] In an alternative embodiment, assuming is the positioning result of the MSSA algorithm, is the positioning result of the ICNN-GRU-Attention algorithm. The final target position Y can be calculated according to the following formula.

[0038] ; in, is the weight, which is measured according to the experiment.

[0039] In one example, different ranges were measured and calculated. The sensor array was used to conduct experiments in the close-range area, which was 5cm-10cm above the array. 96 experimental points were collected at 1cm intervals, for a total of 576 experimental points. The MSSA-ICNN-GRU-Attention fusion positioning algorithm was used for positioning experiments.

[0040] The sensor array was used to conduct experiments in a mixed area. The mixed area was 10cm-15cm above the array. Magnetic field data was collected at 96 experimental points at intervals of 1cm, for a total of 576 experimental points. The MSSA-ICNN-GRU-Attention fusion positioning algorithm was used for positioning experiments.

[0041] The sensor array was used to conduct experiments in the long-distance area, which was the area 15cm-20cm above the array. Magnetic field data was collected at 96 experimental points at intervals of 2cm, for a total of 385 experimental points. The MSSA-ICNN-GRU-Attention fusion positioning algorithm was used for positioning experiments.

[0042] The embodiment of the present application uses a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of the magnetic dipole to be measured, and fuses the MSSA algorithm for close-range positioning and the ICNN-GRU-Attention neural network algorithm for long-range positioning to obtain a fused positioning algorithm, thereby inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fused positioning algorithm to determine the target position of the magnetic dipole to be measured. By dividing the positioning area into a close-range area, a mixed area, and a long-range area, and fusing positioning algorithms with different applicable ranges to generate a matching fusion algorithm, the positioning accuracy of a single target permanent magnet is improved.

[0043] In some embodiments, see Figure 2 , Figure 2 The single target permanent magnet positioning device 200 based on the magnetic dipole model provided in the embodiment of the present application comprises: a data measurement module 210, an algorithm fusion module 220 and a position determination module 230, wherein: The data measurement module 210 is configured to use a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet.

[0044] The algorithm fusion module 220 is configured to fuse the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm.

[0045] The position determination module 230 is configured to input the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

[0046] In some embodiments, the data measurement module 210 is specifically configured as follows: Determine the flux parameters of the magnetic dipole to be measured using the Heimholtz coil and the fluxmeter; A mapping relationship between the magnetic flux parameter and the magnetic moment data is established by Ampere's circuit theorem or the superposition principle of the magnetic field to determine the magnetic moment data of the magnetic dipole to be measured.

[0047] In some embodiments, the algorithm fusion module 220 is specifically configured as follows: The SSA sparrow algorithm is improved by using the mutated cat chaotic mapping, the improvement of the ratio coefficient between the discoverer and the joiner and the update of the discoverer position, and the MSSA algorithm for close-range positioning is obtained; specifically, The population is initialized using the mutated cat chaotic map. The ratio coefficient r of discoverers to joiners is updated once every generation. The update formula of r is as follows: ; in, is the initial scale factor set, is the current iteration number, is the maximum number of iterations set, is the disturbance factor set, is a random number in the interval [0,1]; The discoverer position update formula is improved as follows:

[0048] in, Represents the current iteration number, , represents the maximum number of iterations, Indicates A sparrow in the Location information in the dimension, is a random number, and Represent the warning value and safety value respectively. , , is a random number that follows a normal distribution. Indicates a A matrix where all elements are 1. , the finder can perform a wider search operation if , which means that the discoverer has discovered the predator, and all sparrows need to fly quickly to a safe place to find food.

[0049] In some embodiments, the algorithm fusion module 220 is specifically configured as follows: The convolutional neural network, gated recurrent unit and attention mechanism are combined to obtain the ICNN-GRU-Attention neural network algorithm for long-distance positioning; the loss function of the ICNN-GRU-Attention neural network algorithm is the mean square error: ; in, for A matrix representing the position and posture information predicted by the network, Represents the real position and posture information .

[0050] In some embodiments, the single target permanent magnet positioning device 200 based on the magnetic dipole model further includes a data acquisition module; the data acquisition module is specifically configured as follows: The magnetic dipole to be measured is fixed by using a positioning plate; Multiple magnetic sensors are formed into an array to obtain magnetic field strength data of magnetic dipoles to be measured at different positions.

[0051] In some embodiments, the data acquisition module is specifically configured as follows: The coordinates of the sensors are , , P is the distance from the center of the magnet to the The distance between sensors is represented by the vector ; The magnetic dipole to be measured is The magnetic field strength at each sensor location is: ; in, express The unit vector in the direction of the axis, the magnet The magnetic field strength at the sensor exist The orthogonal components in the axial direction are , the expression is as follows: ; in, is the module of P, .

[0052] In some embodiments, the location determination module 230 is specifically configured as follows: Determine the first target position of the magnetic dipole to be measured by using the MSSA algorithm; The ICNN-GRU-Attention neural network algorithm is used to determine the second target position of the magnetic dipole to be measured; The first target position and the second target position are processed in a weighted manner to determine the target position of the magnetic dipole to be measured.

[0053] The single target permanent magnet positioning device based on the magnetic dipole model provided in the embodiment of the present application can realize each process in the embodiment corresponding to the above-mentioned single target permanent magnet positioning method based on the magnetic dipole model. To avoid repetition, they will not be described here.

[0054] It should be noted that the single-target permanent magnet positioning device based on the magnetic dipole model provided in the embodiment of the present application and the single-target permanent magnet positioning method based on the magnetic dipole model provided in the embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned single-target permanent magnet positioning method based on the magnetic dipole model, and the repeated parts will not be repeated.

[0055] In some embodiments, see Figure 3 , Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. An electronic device 300 provided in an embodiment of the present application includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program implements the above-mentioned single target permanent magnet positioning method based on the magnetic dipole model when executed by the processor. Specifically, the processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 310 may also include an onboard memory for cache purposes. The processor 310 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiment of the present application. The memory 320 may be any medium capable of containing, storing, conveying, propagating or transmitting instructions. For example, the memory 320 may include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device, component or propagation medium. Specific examples of the memory 320 include: a magnetic storage device, such as a magnetic tape or a hard disk (HDD); an optical storage device, such as a compact disk (CD-ROM); a random access memory (RAM) or a flash memory; and / or a wired / wireless communication link. The present application also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned single-target permanent magnet positioning method based on a magnetic dipole model is implemented. The computer-readable medium may be included in the device / apparatus / system described in the above-mentioned embodiment; or it may exist alone and not be assembled into the device / apparatus / system. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed, the method according to the embodiment of the present application is implemented. According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above. Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-described embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A single target permanent magnet positioning method based on a magnetic dipole model, characterized in that: include: A uniform magnetic field is generated by a Heimholtz coil to measure the magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet; The MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning are integrated to obtain a fusion positioning algorithm. The magnetic moment data and the magnetic field strength data received by the sensor array are input into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

2. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 1 is characterized in that: The method of using a Heimholtz coil to generate a uniform magnetic field to measure magnetic moment data of a magnetic dipole to be measured comprises: Determine the magnetic flux parameters of the magnetic dipole to be measured by using a Heimholtz coil and a fluxmeter; A mapping relationship between the magnetic flux parameter and the magnetic moment data is established by Ampere's circuit theorem or the superposition principle of magnetic fields to determine the magnetic moment data of the magnetic dipole to be measured.

3. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 1 is characterized in that: The MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning are fused to obtain a fused positioning algorithm, and further includes: The SSA sparrow algorithm is improved by using the mutated cat chaotic mapping, the improvement of the ratio coefficient between the discoverer and the joiner and the update of the discoverer position, and the MSSA algorithm for close-range positioning is obtained; specifically, The population is initialized using the mutated cat chaotic map. The ratio coefficient r of discoverers to joiners is updated once every generation. The update formula of r is as follows: ； in, is the initial scale factor set, is the current iteration number, is the maximum number of iterations set, is the disturbance factor set, is a random number in the interval [0,1]; The discoverer position update formula is improved as follows: ； in, Represents the current iteration number, , represents the maximum number of iterations, Indicates A sparrow in the Location information in the dimension, is a random number, and Represent the warning value and safety value respectively. , , is a random number that follows a normal distribution. Indicates a A matrix where all elements are 1. , the finder can perform a wider search operation if , which means that the discoverer has discovered the predator, and all sparrows need to fly quickly to a safe place to find food.

4. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 1 is characterized in that: The MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning are fused to obtain a fused positioning algorithm, and further includes: The convolutional neural network, gated recurrent unit and attention mechanism are combined to obtain the ICNN-GRU-Attention neural network algorithm for long-distance positioning; the loss function of the ICNN-GRU-Attention neural network algorithm is the mean square error: ； in, for A matrix representing the position and posture information predicted by the network, Represents the real position and posture information .

5. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 1 is characterized in that: Before inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured, the method further includes: Fixing the magnetic dipole to be measured by using a positioning plate; A plurality of magnetic sensors are formed into an array to obtain magnetic field intensity data of the magnetic dipole to be measured at different positions.

6. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 5 is characterized in that: Set up The coordinates of the sensors are , , P is the distance from the center of the magnet to the The distance between sensors is represented by the vector The magnetic dipole to be measured is The magnetic field strength at each sensor location is: ； in, express The unit vector in the direction of the axis, the magnet The magnetic field strength at the sensor exist The orthogonal components in the axial direction are , the expression is as follows: ； in, is the module of P, .

7. The single target permanent magnet positioning method based on the magnetic dipole model according to claim 1 is characterized in that: The step of inputting the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured includes: Determine the first target position of the magnetic dipole to be measured by using the MSSA algorithm; Determine the second target position of the magnetic dipole to be measured by using an ICNN-GRU-Attention neural network algorithm; The first target position and the second target position are processed in a weighted manner to determine the target position of the magnetic dipole to be measured.

8. A single target permanent magnet positioning device based on a magnetic dipole model, characterized in that: include: Data measurement module, algorithm fusion module and location determination module, among which, The data measurement module is configured to use a Heimholtz coil to generate a uniform magnetic field to measure the magnetic moment data of a magnetic dipole to be measured; the magnetic dipole to be measured is a cylindrical permanent magnet; The algorithm fusion module is configured to fuse the MSSA algorithm for short-distance positioning and the ICNN-GRU-Attention neural network algorithm for long-distance positioning to obtain a fused positioning algorithm; The position determination module is configured to input the magnetic moment data and the magnetic field strength data received by the sensor array into the fusion positioning algorithm to determine the target position of the magnetic dipole to be measured.

9. An electronic device comprising a processor and a memory; the memory stores a computer program, wherein: When the computer program is executed by the processor, the computer program implements the single-target permanent magnet positioning method based on a magnetic dipole model according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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