Magnetic field control design method suitable for small-scale carrier

Abstract

The invention belongs to the technical field of magnetic field precision measurement, and provides a magnetic field control design method suitable for a small-scale carrier, which comprises five steps of carrier component magnetic field measurement, modeling, system magnetic control design, simulation optimization and test verification. Firstly, a magnetic field measuring device is used for acquiring magnetic field distribution data of each component in a carrier; 2, performing multi-magnetic dipole magnetic field modeling on each component by adopting a genetic algorithm, and accurately describing magnetic field distribution of the components; and 3, selecting the component made of a non-magnetic or low-magnetic material, or reducing the magnetic interference of the carrier component in modes of demagnetization, magnetic shielding, artificial magnetic source counteracting and the like. And 4, carrying out simulation optimization on the component layout of the carrier by using an adaptive weight particle swarm optimization algorithm so as to minimize the magnetic interference of the system. And 5, constructing an experimental carrier prototype, carrying out complete machine test by using a magnetic field measuring device, and evaluating a magnetic control design effect. The method can effectively control the self magnetic interference of the carrier, improves the magnetic detection performance of the system, is especially suitable for the magnetic control design of small-scale carriers, and provides technical support for the magnetic detection application of carriers such as unmanned aerial vehicles / underwater vehicles / satellites.

Description

Technical Field

[0001] The invention belongs to the technical field of magnetic field precision measurement, and in particular relates to a magnetic field control design method suitable for small-scale carriers. Background Art

[0002] Traditionally, when designing magnetic control for drones, submarines, satellites and other carriers, most of them increase the distance between the magnetic probe and the carrier based on the law that the magnetic field strength decays with the cube of the distance, so as to reduce the interference of the carrier itself on the magnetic probe. There is still no effective solution to the problem of small-scale space magnetic interference control, which is mainly reflected in:

[0003] Existing technologies rely on simple magnetic field measurement tools or methods, which are unable to accurately obtain the magnetic field distribution of complexly distributed components in small-scale space, resulting in inaccurate data for subsequent magnetic field modeling and magnetic control design.

[0004] The prior art uses a single magnetic dipole model to model the magnetic field, which is also inaccurate in small-scale spaces where there are many components in the carrier and the magnetic field distribution is complex. The application of genetic algorithms can solve the problems of model complexity and accuracy, but it has not been applied in the prior art.

[0005] The existing technology mainly relies on increasing the distance between the magnetic probe and the carrier to reduce magnetic interference, which cannot be effectively implemented in small-scale spaces. At the same time, there is no effective demagnetization method for components of hard and soft residual magnetic materials. In addition, traditional magnetic shielding materials occupy a large volume and are not suitable for use in small-scale spaces.

[0006] The existing technology lacks simulation optimization methods for the layout of components inside small-scale spaces, especially the optimization algorithms that can handle multiple independent variables and have adaptive weights. Traditional optimization methods cannot effectively handle the complexity and diversity of system-level magnetic control variables. Summary of the invention

[0007] In view of this, the present invention proposes a magnetic field control design method suitable for small-scale carriers, which includes:

[0008] S1, component magnetic field measurement, obtaining the magnetic field of each component in the carrier and the magnetic field distribution of the carrier as a whole;

[0009] S2, component magnetic field modeling, multi-magnetic dipole magnetic field modeling of each component to accurately describe the magnetic field distribution of the component;

[0010] S3, magnetic control design of components, select non-magnetic or low-magnetic materials to make components, or reduce the magnetic interference of carrier components by demagnetization, magnetic shielding, artificial magnetic source offset, etc.;

[0011] S4, system simulation optimization, simulates and optimizes the layout of the components of the carrier to minimize the magnetic interference of the system;

[0012] S5, test verification, build an experimental carrier prototype to test the whole machine and evaluate the effect of the magnetron design.

[0013] In a preferred solution, the specific process of step S1 includes:

[0014] S11, using a magnetic field measuring device to measure the magnetic field of each component of the carrier;

[0015] S12, recording and collating the magnetic field data of each component to form an initial magnetic field distribution map.

[0016] In a preferred solution, the process of step S2 includes:

[0017] S21, using genetic algorithm to search for inversion model parameters, taking the mean square error between measured and simulated values ​​as the objective function;

[0018] S22, based on the magnetic field measurement data, the magnetic moment and position are optimized by algorithm inversion for multiple iterations;

[0019] S23, after multiple iterations, an optimal multi-magnetic dipole model is obtained, which can more accurately describe the magnetic field distribution of each component of the carrier.

[0020] In a preferred solution, the specific process of step S2 includes:

[0021] S21, initialize the parameters of the genetic algorithm, including population size, crossover rate, mutation rate, and number of iterations; use the mean square error between the measured value and the simulated value as the objective function to evaluate the accuracy of the model; encode the model parameters, including magnetic moment and position, as chromosomes in the genetic algorithm; calculate the fitness of each chromosome, that is, a set of parameters, that is, evaluate the matching degree between the corresponding model and the actual measured data; select the better chromosome according to the fitness to enter the next generation, in preparation for the subsequent crossover and mutation operations;

[0022] S22, crossover the selected chromosomes to generate new chromosomes, i.e., new parameter combinations; mutate the newly generated chromosomes to introduce new genetic diversity; repeat the selection, crossover and mutation operations, and each iteration aims to optimize the objective function, i.e., reduce the mean square error between the measured value and the simulated value; in each iteration, adjust the magnetic moment and position parameters according to the results of the genetic algorithm to more accurately simulate the magnetic field distribution of each component;

[0023] S23, after multiple iterations, selects the chromosome with the highest fitness, that is, the optimal combination of magnetic moment and position parameters; uses these optimal parameters to construct a multi-magnetic dipole model, which can more accurately describe the magnetic field distribution of each component.

[0024] In a preferred solution, the process of step S3 includes:

[0025] S31, selecting a non-magnetic or low-magnetic material to make a carrier component;

[0026] S32, for components made of hard and soft residual magnetic materials, demagnetization is used to reduce magnetic interference;

[0027] S33, magnetic field isolation is performed in the form of surface coating of components, and multiple layers of composite magnetic field shielding materials are deposited on the surface of components in layers, and the radiation leakage magnetic field energy is reduced by multiple magnetic field energy diversion and guidance, thereby reducing its magnetic interference;

[0028] S34, for the components whose magnetic interference cannot be reduced by the above method, reduce their magnetic interference by offsetting it with artificial magnetic sources.

[0029] In a preferred solution, the specific process of step S3 includes:

[0030] S31, selecting non-magnetic or low-magnetic materials to reduce the interference of components with the magnetic field;

[0031] S32, identifying all components of hard remanent magnetic and soft remanent magnetic materials used in the carrier; performing demagnetization treatment on these components to reduce their remanent magnetic properties;

[0032] S33, select magnetic field isolation coating, the coating thickness is generally 3mm; select multi-layer composite magnetic field shielding material, and deposit the multi-layer composite magnetic field shielding material on the surface of the component; through multiple magnetic field energy diversion and guidance, reduce the magnetic field energy of radiation leakage and improve the magnetic field shielding effect;

[0033] S34, identifying components whose magnetic interference cannot be reduced by the above method due to functional or structural limitations; and using artificial magnetic sources to offset the magnetic interference.

[0034] In a preferred solution, the process of step S4 includes:

[0035] S41, using adaptive weighted particle swarm algorithm to perform adaptive weighted permutations and combinations of multiple independent variables;

[0036] S42, according to the multi-magnetic dipole model of each component, taking the spatial position of the magnetic dipole model as an input variable, and taking the magnitude of the magnetic field at the location where the magnetic probe is installed as an output variable;

[0037] S43, establishing an objective function with the internal space of the carrier as a constraint condition, and solving the minimum value of the objective function with multiple independent variables;

[0038] S44, iterative optimization is performed through the adaptive weighted particle swarm algorithm to find the optimal component layout solution.

[0039] In a preferred solution, the specific process of step S4 includes:

[0040] S411, algorithm initialization, initializing the parameters of the adaptive weighted particle swarm algorithm, including the number of particles, speed limit, position limit, and number of iterations;

[0041] S412, adaptive weight setting, setting different weight parameters according to the magnetic field size of each component to reflect the importance of each component to the total magnetic field;

[0042] S413, particle encoding, taking the position of the carrier component as the position variable of the particle, each particle represents a possible component layout scheme;

[0043] S414, fitness function definition, defines the fitness function, which is usually the magnetic field size at the location where the magnetic probe is installed, and the goal is to minimize this value;

[0044] S421, model input, using the multi-magnetic dipole model obtained in step S2, and taking the spatial position of each magnetic dipole as an input variable for simulation;

[0045] S422, simulation calculation, calculating the magnetic field size at the location where the magnetic probe is installed under the current component layout through simulation software;

[0046] S423, output analysis, using the simulation results as the fitness value of the particle for the subsequent optimization process;

[0047] S431, constraint definition, defining the physical size and shape of the internal space of the carrier as constraint conditions for layout optimization;

[0048] S432, objective function construction, constructing an objective function, which is usually to minimize the magnetic field size at the location where the magnetic probe is installed while satisfying the constraints of the internal space of the carrier;

[0049] S433, solving the optimization problem, using an adaptive weighted particle swarm algorithm to solve the optimization problem, and finding a component layout solution that meets the constraints so that the objective function value is minimized;

[0050] S441, particle update, in each iteration, the speed and position of the particles are updated according to the adaptive weights to find a better component layout solution;

[0051] S442, fitness evaluation, evaluates the fitness of the new layout scheme, that is, the magnitude of the magnetic field at the location where the magnetic probe is installed;

[0052] S443, global optimal and individual optimal update, update the individual optimal solution of each particle and the global optimal solution of the entire particle swarm according to the fitness value;

[0053] S444, iterate until convergence, repeat the iterative process until a predetermined number of iterations is reached or the fitness value converges, and the global optimal solution at this time is the optimal component layout solution.

[0054] The beneficial effects achieved by the present invention are:

[0055] First, the present invention uses a magnetic field measuring device to accurately measure the magnetic field of each component of the carrier, records and organizes the magnetic field data of each component, and forms an initial magnetic field distribution map, which provides a reliable data basis for subsequent magnetic field modeling and magnetron design. The genetic algorithm is used for multi-magnetic dipole magnetic field modeling, and the optimal model is obtained through multiple iterative optimizations, which can more accurately describe the magnetic field distribution of the carrier component. This high-precision modeling technology helps to improve the accuracy of magnetron design.

[0056] Second, choose non-magnetic or low-magnetic materials to make carrier components to reduce magnetic interference from the source. Demagnetize components made of hard and soft residual magnetic materials to further reduce their residual magnetism and reduce magnetic interference. Magnetic field isolation is carried out in the form of surface plating of components. Multi-layer composite magnetic field shielding materials are deposited in layers on the surface of components to reduce the magnetic field energy of radiation leakage and improve the magnetic field shielding effect. For components whose magnetic interference cannot be reduced by the above methods, their magnetic interference is reduced by offsetting with artificial magnetic sources.

[0057] Third, the layout of the components of the carrier is optimized through simulation software, and the adaptive weighted particle swarm algorithm is used for iterative optimization to find the optimal component layout solution to minimize the magnetic interference of the system. This optimization method can make full use of small-scale space and improve the overall performance of the carrier. The experimental carrier prototype is built, and the magnetic field measurement device is used for overall testing to verify the effectiveness of the magnetic control design. This test verification method can ensure the effect of the magnetic control design in practical applications and improve the reliability and practicality of the design.

[0058] Fourth, through the method of the present invention, the magnetic interference effect of the carrier itself is effectively controlled, and the magnetic detection performance of the system is maximized. The method of the present invention is particularly suitable for the magnetic control design of small-scale carriers, and provides technical support for the magnetic detection application of carriers such as drones / submarine / satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Shown is the magnetron design flow chart.

[0060] Figure 2 Shown is a flow chart of the magnetic field measurement method.

[0061] Figure 3 Shown is a flow chart of the component magnetic field modeling method.

[0062] Figure 4 It is a schematic diagram of the structure of a multi-layer composite magnetic field shielding material.

[0063] Figure 5 It is the flow chart of particle swarm algorithm based on adaptive weight. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative, and the present invention is not limited to the various structures recorded in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0065] Reference Figure 1-Figure 5 In order to effectively control the influence of the carrier's own magnetic interference, a multi-point rotation measurement method is used to model the magnetic field of each component of the carrier, provide technical support for the magnetic control design of the carrier components, and simulate and optimize the layout of the components in the small-scale carrier, so as to achieve the purpose of minimizing the magnetic interference of the carrier itself. The steps are as follows:

[0066] Magnetic field measurement of components:

[0067] The magnetic field of each component in the carrier and the magnetic field distribution of the carrier as a whole are obtained by measuring the magnetic field of each component, providing data support for subsequent modeling and magnetron design.

[0068] Magnetic field modeling of internal components:

[0069] Limited by the small-scale space of the carrier, the magnetic field modeling of the carrier components cannot use a single magnetic dipole model. The present invention uses a genetic algorithm to model the magnetic field of the components with multiple magnetic dipoles.

[0070] The process is as follows: First, a genetic algorithm is used to search for inversion model parameters, and the mean square error between the measured value and the simulated value is used as the objective function. According to the magnetic field measurement data, the magnetic moment and position are double-iterated and optimized through algorithm inversion to obtain the optimal multi-magnetic dipole model. The method flow is shown below.

[0071] Components magnetron design:

[0072] Try to use non-magnetic or low-magnetic materials to make carrier components, and use demagnetization, magnetic shielding or artificial magnetic source offset to control the magnetic field of the carrier's hard and soft residual magnetic components.

[0073] Different from the traditional magnetic shielding shell, this project uses the surface coating of the components to isolate the magnetic field. The coating thickness is generally 3mm. On the basis of ensuring the magnetic shielding effectiveness, the components are installed in situ and do not occupy the volume. This method deposits multiple layers of composite magnetic field shielding materials on the surface of the components in layers. Through multiple magnetic field energy diversion and guidance, magnetic fields of different energies are transmitted into each layer, minimizing the radiation leakage magnetic field energy, and thus achieving the application effect of magnetic field shielding.

[0074] For components whose magnetic interference cannot be reduced by the above method, their magnetic interference can be reduced by offsetting it with artificial magnetic sources.

[0075] System magnetic field simulation optimization:

[0076] In view of the large number of system-level magnetic control variables, an adaptive weighted particle swarm algorithm is used to adaptively arrange and combine multiple independent variables to find the optimal solution for component layout.

[0077] Component layout optimization process: According to the multi-magnetic dipole model of each component, the spatial position of the magnetic dipole model is used as the input variable, the magnetic field size at the location where the magnetic probe is installed is used as the output variable, and the objective function is established with the space inside the carrier as the constraint condition to solve the minimum value of the objective function with multiple independent variables. However, since the solution process generally randomizes the independent variables, it is not conducive to component layout optimization. The adaptive weighted particle swarm algorithm sets different weight parameters according to the magnetic field size of each component, and prioritizes the position according to the weight size to optimize the component layout. The optimization process is shown in the figure:

[0078] Test verification:

[0079] A prototype of the experimental carrier was built and tested using a magnetic field measurement device to verify the effectiveness of the magnetic control design.

[0080] Example 1: This embodiment introduces in detail the detailed process of a magnetic field control design method suitable for small-scale carriers.

[0081] 1. Magnetic field measurement of components

[0082] Step S1: Use a magnetic field measuring device to measure the magnetic field of each component of the carrier.

[0083] S11: Place the components of the carrier one by one on the turntable, ensuring that the components can rotate 360 ​​degrees so as to fully measure their magnetic field distribution.

[0084] S12: Start the magnetic field measurement device to record the magnetic field data of each component during the rotation process. Through the data processing software, the measurement data is sorted into an initial magnetic field distribution map to provide data support for subsequent modeling.

[0085] 2. Magnetic field modeling of components

[0086] Step S2: Using genetic algorithm to model the magnetic field of multiple magnetic dipoles.

[0087] S21: Initialize the parameters of the genetic algorithm, including population size, crossover rate, mutation rate, and number of iterations. The mean square error between the measured value and the simulated value is used as the objective function to evaluate the accuracy of the model.

[0088] S22: Encode the magnetic moment and position parameters as chromosomes in the genetic algorithm, and search for inversion model parameters through the genetic algorithm. In each iteration, the magnetic moment and position parameters are adjusted according to the algorithm results to optimize the matching degree between the model and the actual measurement data.

[0089] S23: After multiple iterations, the chromosome with the highest fitness is selected, that is, the optimal combination of magnetic moment and position parameters. These optimal parameters are used to construct a multi-magnetic dipole model, which can more accurately describe the magnetic field distribution of the carrier component.

[0090] 3. Magnetic control design of all components

[0091] Step S3: The magnetic interference of the carrier component is controlled by selecting non-magnetic or low-magnetic materials to make the component, or by demagnetization, magnetic shielding, artificial magnetic source offset, etc.

[0092] S31: When designing the carrier, try to choose non-magnetic or low-magnetic materials to make components to reduce magnetic interference.

[0093] S32: Identify the components of hard remanent magnetic and soft remanent magnetic materials used in the carrier, and treat these components by demagnetizing them to reduce their magnetic interference.

[0094] S33: Magnetic field isolation is achieved by coating the surface of components. Multi-layer composite magnetic field shielding materials are selected and deposited on the surface of components in layers. The coating thickness is generally 3mm. Through multiple magnetic field energy diversion and guidance, the radiation leakage magnetic field energy is reduced and the magnetic field shielding effect is improved.

[0095] S34: For the components whose magnetic interference cannot be reduced by the above method, an artificial magnetic source is used to reduce its magnetic interference. Through precise calculation, a reverse artificial magnetic source is installed at a suitable position to offset the magnetic interference of the original component.

[0096] 4. System magnetic field simulation optimization

[0097] Step S4: Optimizing the layout of components of the carrier by simulation software to minimize magnetic interference.

[0098] S41: Adopting the adaptive weighted particle swarm algorithm to perform adaptive weighted permutations and combinations on multiple independent variables. Initializing the parameters of the adaptive weighted particle swarm algorithm, including the number of particles, speed limit, position limit, and number of iterations.

[0099] S42: According to the multi-magnetic dipole model of each component, the spatial position of the magnetic dipole model is used as an input variable, and the magnitude of the magnetic field at the location where the magnetic probe is installed is used as an output variable.

[0100] S43: Establish an objective function with the internal space of the carrier as a constraint condition, and solve the minimum value of the objective function with multiple independent variables. When constructing the objective function, the physical size and shape constraints of the internal space of the carrier need to be considered.

[0101] S44: Iterative optimization is performed using an adaptive weighted particle swarm algorithm. In each iteration, the speed and position of the particles are updated according to the adaptive weights to find a better layout of the components. The fitness of the new layout (i.e., the magnetic field size at the location where the magnetic probe is installed) is evaluated, and the global optimal solution and the individual optimal solution are updated. The iterative process is repeated until convergence or the predetermined number of iterations is reached.

[0102] 5. Test verification

[0103] The magnetic field measurement device is used to conduct actual measurements on the experimental carrier prototype to verify the effectiveness of the magnetron design. The measured data is compared with the simulation data to evaluate the actual effect of the magnetron design, and adjustments and optimizations are made as needed.

[0104] Through the above detailed process, the method of the present invention can realize the precise magnetic control design of small-scale carriers, effectively control magnetic interference, and improve carrier performance.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A magnetic field control design method suitable for small-scale carriers, characterized in that: It includes: S1, component magnetic field measurement, obtaining the magnetic field of each component in the carrier and the magnetic field distribution of the carrier as a whole; S2, component magnetic field modeling, multi-magnetic dipole magnetic field modeling of each component to accurately describe the magnetic field distribution of the component; S3, magnetic control design of components, select components with non-magnetic or low-magnetic materials, or reduce the magnetic interference of carrier components by demagnetization, magnetic shielding, artificial magnetic source offset, etc. S4, system simulation optimization, simulates and optimizes the layout of the components of the carrier to minimize the magnetic interference of the system; S5, test verification, build an experimental carrier prototype to test the whole machine and evaluate the effect of the magnetron design.

2. A magnetic field control design method suitable for small-scale carriers according to claim 1, characterized in that: The specific process of step S1 includes: S11, using a magnetic field measuring device to measure the magnetic field of each component of the carrier; S12, recording and collating the magnetic field data of each component to form an initial magnetic field distribution map.

3. A magnetic field control design method suitable for small-scale carriers according to claim 1, characterized in that: The process of step S2 includes: S21, using genetic algorithm to search for inversion model parameters, taking the mean square error between measured and simulated values ​​as the objective function; S22, based on the magnetic field measurement data, the magnetic moment and position are optimized by algorithm inversion for multiple iterations; S23, after multiple iterations, an optimal multi-magnetic dipole model is obtained, which can more accurately describe the magnetic field distribution of each component of the carrier.

4. A magnetic field control design method suitable for small-scale carriers according to claim 3, characterized in that: The specific process of step S2 includes: S21, initialize the parameters of the genetic algorithm, including population size, crossover rate, mutation rate, and number of iterations; use the mean square error between the measured value and the simulated value as the objective function to evaluate the accuracy of the model; encode the model parameters, including magnetic moment and position, as chromosomes in the genetic algorithm; calculate the fitness of each chromosome, that is, a set of parameters, that is, evaluate the matching degree between the corresponding model and the actual measured data; select the better chromosome according to the fitness to enter the next generation, in preparation for the subsequent crossover and mutation operations; S22, crossover the selected chromosomes to generate new chromosomes, i.e., new parameter combinations; mutate the newly generated chromosomes to introduce new genetic diversity; repeat the selection, crossover and mutation operations, and each iteration aims to optimize the objective function, i.e., reduce the mean square error between the measured value and the simulated value; in each iteration, adjust the magnetic moment and position parameters according to the results of the genetic algorithm to more accurately simulate the magnetic field distribution of each component; S23, after multiple iterations, selects the chromosome with the highest fitness, that is, the optimal combination of magnetic moment and position parameters; uses these optimal parameters to construct a multi-magnetic dipole model, which can more accurately describe the magnetic field distribution of each component.

5. The magnetic field control design method suitable for small-scale carriers according to claim 1, characterized in that: The process of step S3 includes: S31, selecting a non-magnetic or low-magnetic material to make a carrier component; S32, for components made of hard and soft residual magnetic materials, demagnetization is used to reduce magnetic interference; S33, magnetic field isolation is performed in the form of plating, and multiple layers of composite magnetic field shielding materials are deposited on the surface of the components in layers, and the radiation leakage magnetic field energy is reduced by multiple magnetic field energy diversion and guidance, thereby reducing its magnetic interference; S34, for the components whose magnetic interference cannot be reduced by the above method, reduce their magnetic interference by offsetting it with artificial magnetic sources.

6. A magnetic field control design method suitable for small-scale carriers according to claim 5, characterized in that: The specific process of step S3 includes: S31, selecting non-magnetic or low-magnetic materials to reduce the interference of components with the magnetic field; S32, identifying all components of hard remanent magnetic and soft remanent magnetic materials used in the carrier; performing demagnetization treatment on these components to reduce their remanent magnetic properties; S33, select magnetic field isolation coating, the coating thickness is generally 3mm; select multi-layer composite magnetic field shielding material, and deposit the multi-layer composite magnetic field shielding material on the surface of the component; through multiple magnetic field energy diversion and guidance, reduce the magnetic field energy of radiation leakage and improve the magnetic field shielding effect; S34, identifying components whose magnetic interference cannot be reduced by the above method due to functional or structural limitations; and using artificial magnetic sources to offset the magnetic interference.

7. A magnetic field control design method suitable for small-scale carriers according to claim 4, characterized in that: The process of step S4 includes: S41, using adaptive weighted particle swarm algorithm to perform adaptive weighted permutations and combinations of multiple independent variables; S42, according to the multi-magnetic dipole model of each component, taking the spatial position of the magnetic dipole model as an input variable, and taking the magnitude of the magnetic field at the location where the magnetic probe is installed as an output variable; S43, establishing an objective function with the internal space of the carrier as a constraint condition, and solving the minimum value of the objective function with multiple independent variables; S44, iterative optimization is performed through the adaptive weighted particle swarm algorithm to find the optimal component layout solution.

8. A magnetic field control design method suitable for small-scale carriers according to claim 7, characterized in that: The specific process of step S4 includes: S411, algorithm initialization, initializing the parameters of the adaptive weighted particle swarm algorithm, including the number of particles, speed limit, position limit, and number of iterations; S412, adaptive weight setting, setting different weight parameters according to the magnetic field size of each component to reflect the importance of each component to the total magnetic field; S413, particle encoding, taking the position of the carrier component as the position variable of the particle, each particle represents a possible component layout scheme; S414, fitness function definition, defines the fitness function, which is usually the magnetic field size at the location where the magnetic probe is installed, and the goal is to minimize this value; S421, model input, using the multi-magnetic dipole model obtained in step S2, and taking the spatial position of each magnetic dipole as an input variable for simulation; S422, simulation calculation, calculating the magnetic field size at the location where the magnetic probe is installed under the current component layout through simulation software; S423, output analysis, using the simulation results as the fitness value of the particle for the subsequent optimization process; S431, constraint definition, defining the physical size and shape of the internal space of the carrier as constraint conditions for layout optimization; S432, objective function construction, constructing an objective function, which is usually to minimize the magnetic field size at the location where the magnetic probe is installed while satisfying the constraints of the internal space of the carrier; S433, solving the optimization problem, using an adaptive weighted particle swarm algorithm to solve the optimization problem, and finding a component layout solution that meets the constraints so that the objective function value is minimized; S441, particle update, in each iteration, the speed and position of the particles are updated according to the adaptive weights to find a better component layout solution; S442, fitness evaluation, evaluates the fitness of the new layout scheme, that is, the magnitude of the magnetic field at the location where the magnetic probe is installed; S443, global optimal and individual optimal update, update the individual optimal solution of each particle and the global optimal solution of the entire particle swarm according to the fitness value; S444, iterate until convergence, repeat the iterative process until a predetermined number of iterations is reached or the fitness value converges, and the global optimal solution at this time is the optimal component layout solution.

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