Magnetic core material determination method and device of fluxgate, computer equipment, readable storage medium and program product

Abstract

The invention relates to a magnetic core material determination method and device of a fluxgate, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of electromagnetism. The method comprises the following steps: acquiring characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters comprise one or more of magnetic conductivity and magnetic induction intensity; generating a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters; the fluxgate sensor model takes any candidate magnetic core material as a magnetic core; simulating each fluxgate sensor model by adopting a plurality of preset excitation voltages, and determining a relation curve between a performance evaluation value and a characteristic parameter of each fluxgate sensor model; and determining an optimal magnetic core material from the plurality of candidate magnetic core materials according to a preset selection rule and a relation curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model. By adopting the method, the adaptability of the selected magnetic core material can be improved.

Description

Technical Field

[0001] The present application relates to the field of electromagnetic technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for determining a magnetic core material of a fluxgate. Background Art

[0002] The core principle of the fluxgate sensor is to use the principle that the external magnetic field affects the magnetization of the magnetic core to achieve magnetic field measurement. The structure is mainly composed of a magnetic core, an excitation coil and a detection coil. With the development of electronic technology, higher-performance soft magnetic materials such as Permalloy and amorphous materials have begun to be used as magnetic cores. These materials have higher magnetic permeability and saturation magnetic induction intensity, thereby improving the sensitivity and resolution of the sensor. Soft magnetic materials have low coercive force and high magnetic permeability, which are suitable for fluxgate sensors. However, the current analysis and selection methods for soft magnetic materials mainly analyze the various characteristics of the core material itself and rely on the experience of technicians to evaluate the optimal core material, which is less in line with actual application requirements. Therefore, there is a problem of low adaptability in the selection of core materials. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining the core material of a fluxgate, which can improve the adaptability of the selected core material, in order to address the above technical problems.

[0004] In a first aspect, the present application provides a method for determining a magnetic core material of a fluxgate, comprising:

[0005] Acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity;

[0006] Generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as the magnetic core;

[0007] Using a plurality of preset excitation voltages, a simulation operation is performed on each of the fluxgate sensor models to determine a relationship curve between a performance evaluation value of each of the fluxgate sensor models and the characteristic parameter;

[0008] According to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, the optimal magnetic core material is determined from the plurality of candidate magnetic core materials according to a preset selection rule.

[0009] In one embodiment, the performance evaluation value includes sensitivity, and the simulation operation is performed on each fluxgate sensor model using a plurality of preset excitation voltages to determine a relationship curve between the performance evaluation value of each fluxgate sensor model and the characteristic parameter, including:

[0010] Using a plurality of preset excitation voltages, respectively, a simulation operation is performed on each fluxgate sensor model to obtain a plurality of sets of correlation data between the magnetic permeability and the sensitivity, and a plurality of sets of correlation data between the magnetic induction intensity and the sensitivity;

[0011] Generate corresponding first relationship curves and second relationship curves respectively according to multiple sets of correlation data between the magnetic permeability and the sensitivity, and multiple sets of correlation data between the magnetic induction intensity and the sensitivity;

[0012] A relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model is determined according to the first relationship curve and the second relationship curve.

[0013] In one embodiment, the performance evaluation value further includes output signal strength, and determining the relationship curve between the performance evaluation value of each fluxgate sensor model and the characteristic parameter according to the first relationship curve and the second relationship curve includes:

[0014] Determining a third relationship curve between the magnetic induction intensity and the output signal intensity according to the first relationship curve and the second relationship curve;

[0015] The first relationship curve, the second relationship curve and the third relationship curve are used as relationship curves between the performance evaluation value and the characteristic parameter of each fluxgate sensor model.

[0016] In one embodiment, determining a third relationship curve between the magnetic induction intensity and the output signal intensity according to the first relationship curve and the second relationship curve includes:

[0017] Determine the preset excitation voltage corresponding to the maximum value of the sensitivity according to the first relationship curve and the second relationship curve as a saturation excitation voltage;

[0018] Using the saturation excitation voltage, respectively perform simulation operations on each fluxgate sensor model to obtain multiple groups of correlation data between the magnetic induction intensity and the output signal intensity;

[0019] A third relationship curve is generated according to a plurality of sets of correlation data between the magnetic induction intensity and the output signal intensity.

[0020] In one embodiment, the determining of the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter and a preset selection rule comprises:

[0021] According to a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, selecting the characteristic parameter corresponding to the maximum value of the performance evaluation value of the fluxgate sensor model;

[0022] The candidate magnetic core material corresponding to the characteristic parameter is used as the optimal magnetic core material.

[0023] In one embodiment, generating a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters includes:

[0024] Determining the magnetic flux density of each candidate magnetic core material according to the magnetic permeability of each candidate magnetic core material;

[0025] Determining the core cross-sectional area of ​​each fluxgate sensor model according to the magnetic flux density of each candidate magnetic core material;

[0026] A plurality of fluxgate sensor models are generated according to the preset design parameters, the characteristic parameters of each candidate magnetic core material and the magnetic core cross-sectional area of ​​each fluxgate sensor model.

[0027] In a second aspect, the present application further provides a device for determining a magnetic core material of a fluxgate, comprising:

[0028] A parameter acquisition module, used to acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity;

[0029] A model generation module, used to generate a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as a magnetic core;

[0030] A curve determination module, used to use a plurality of preset excitation voltages to perform simulation operations on each of the fluxgate sensor models, and determine a relationship curve between the performance evaluation value of each of the fluxgate sensor models and the characteristic parameters;

[0031] The material determination module is used to determine the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter and according to a preset selection rule.

[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity;

[0034] Generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as the magnetic core;

[0035] Using a plurality of preset excitation voltages, a simulation operation is performed on each of the fluxgate sensor models to determine a relationship curve between a performance evaluation value of each of the fluxgate sensor models and the characteristic parameter;

[0036] According to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, the optimal magnetic core material is determined from the plurality of candidate magnetic core materials according to a preset selection rule.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity;

[0039] Generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as the magnetic core;

[0040] Using a plurality of preset excitation voltages, a simulation operation is performed on each of the fluxgate sensor models to determine a relationship curve between a performance evaluation value of each of the fluxgate sensor models and the characteristic parameter;

[0041] According to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, the optimal magnetic core material is determined from the plurality of candidate magnetic core materials according to a preset selection rule.

[0042] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0043] Acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity;

[0044] Generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as the magnetic core;

[0045] Using a plurality of preset excitation voltages, a simulation operation is performed on each of the fluxgate sensor models to determine a relationship curve between a performance evaluation value of each of the fluxgate sensor models and the characteristic parameter;

[0046] According to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, the optimal magnetic core material is determined from the plurality of candidate magnetic core materials according to a preset selection rule.

[0047] The above-mentioned fluxgate core material determination method, device, computer equipment, computer-readable storage medium and computer program product can obtain characteristic parameters corresponding to multiple candidate core materials, wherein the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity, so as to understand the performance characteristics of different candidate core materials in many aspects and provide a scientific basis for subsequent material selection. According to the characteristic parameters of each candidate core material and the preset design parameters, multiple fluxgate sensor models are generated; wherein the fluxgate sensor model uses any candidate core material as the core to simulate the performance of each candidate magnetic material in actual application. Using multiple preset excitation voltages, simulation operations are performed on each fluxgate sensor model to determine the relationship curve between the performance evaluation value and the characteristic parameters of each fluxgate sensor model, so as to evaluate the dynamic performance of each candidate core material under different working conditions in actual application. According to the relationship curve between the performance evaluation value and the characteristic parameters of the fluxgate sensor model, the optimal core material is determined from multiple candidate core materials according to the preset selection rules. By analyzing the relationship curve between the performance evaluation value and the characteristic parameters, the key factors affecting the performance of the fluxgate sensor of different candidate core materials can be understood, which provides a quantitative basis for the selection of core materials, reduces the subjectivity of the selection of core materials, and thus improves the adaptability of the selected core materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a schematic flow chart of a method for determining a magnetic core material of a fluxgate in one embodiment;

[0050] Figure 2 A schematic flow chart of a step of determining a relationship curve between a performance evaluation value and the characteristic parameter in one embodiment;

[0051] Figure 3 A schematic diagram of a curve showing the relationship between magnetic permeability and sensitivity in one embodiment;

[0052] Figure 4 A schematic diagram of a curve showing the relationship between magnetic induction intensity and sensitivity in one embodiment;

[0053] Figure 5 A schematic diagram of a curve showing the relationship between magnetic induction intensity and output signal intensity in one embodiment;

[0054] Figure 6 Schematic diagram of a flow chart of a method for analyzing characteristics of a fluxgate core material in one embodiment;

[0055] Figure 7 is a structural block diagram of a device for determining a magnetic core material of a fluxgate in one embodiment;

[0056] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In one embodiment, Figure 1 As shown, a method for determining the magnetic core material of a fluxgate is provided. This embodiment uses the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0059] Step S102, obtaining characteristic parameters corresponding to a plurality of candidate magnetic core materials.

[0060] Among them, the magnetic core material can be a magnetic material commonly used in transformers, inductors and other electromagnetic equipment, and its main function is to concentrate the magnetic field and improve the transmission efficiency of the magnetic flux. The magnetic core material is usually made of ferrite, silicon steel or other such alloys, and has good magnetic permeability and low magnetic loss. The magnetic core material involved in this embodiment can be a soft magnetic material, which is a type of material that can be easily magnetized and demagnetized in an external magnetic field. It can respond quickly after the magnetic field is applied and quickly loses its magnetism after the external magnetic field is removed. Soft magnetic materials have lower coercive force and higher magnetic permeability, so that they show good performance in a changing magnetic field.

[0061] The characteristic parameters include one or more of magnetic permeability and magnetic induction intensity. Magnetic permeability can be a physical quantity describing the material's ability to respond to a magnetic field; magnetic induction intensity can be a physical quantity describing the magnetic field intensity and the magnetic flux density in the magnetic field.

[0062] Optionally, the system obtains characteristic parameters corresponding to multiple candidate magnetic core materials through multiple measurement methods. For example, if the characteristic parameter is magnetic permeability, a Hall effect sensor is used to measure the Hall voltage of the magnetic core material under a known magnetic field, and the magnetic permeability is calculated based on the Hall voltage; if the characteristic parameter is magnetic induction intensity, a soft magnetic material analysis instrument is used to test the saturation magnetic induction intensity, magnetic fields of different intensities are applied, and the corresponding magnetic induction intensity is recorded.

[0063] Step S104: generating a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters.

[0064] The fluxgate sensor model uses any candidate core material as the core.

[0065] Among them, the fluxgate sensor can be a high-sensitivity magnetic field sensor, which is mainly used to measure the magnetic field strength, especially under low frequency and DC conditions, and works based on the Hall effect and the characteristics of the magnetic core material.

[0066] The preset design parameters may be structural parameters and property parameters of the fluxgate sensor set before modeling.

[0067] Optionally, the system configures the magnetic core of the fluxgate sensor model according to the characteristic parameters of each candidate magnetic core material, and establishes the structure and properties of the fluxgate sensor model according to preset design parameters. Furthermore, the system performs modeling through electromagnetic simulation software to obtain the fluxgate sensor model corresponding to each candidate magnetic core material as the magnetic core.

[0068] Step S106 , using a plurality of preset excitation voltages, performing simulation operations on each fluxgate sensor model, and determining a relationship curve between a performance evaluation value and a characteristic parameter of each fluxgate sensor model.

[0069] The excitation voltage may be a voltage applied to the fluxgate sensor model to provide a working voltage, stimulate the response of the fluxgate sensor model, and obtain an output signal. The performance evaluation value may be the performance of the fluxgate sensor model evaluated based on the response of the fluxgate sensor model.

[0070] Optionally, the system uses a plurality of preset excitation voltages, which may be excitation voltages of different levels according to a certain size rule, to perform simulation operations on each fluxgate sensor model, so that each fluxgate sensor model collects the signal output by the fluxgate sensor model when each preset excitation voltage is applied. The performance evaluation value of the fluxgate sensor model is determined based on the output signal, thereby generating a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameters of the candidate magnetic core material corresponding to the fluxgate sensor model.

[0071] Step S108 , determining the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model and a preset selection rule.

[0072] Among them, the preset selection rule can be an evaluation criterion for selecting the priority of the candidate magnetic core material. For example, the higher the performance evaluation value of the fluxgate sensor model corresponding to the candidate magnetic core material, the higher the priority of the candidate magnetic core material in being selected as the optimal magnetic core material.

[0073] Optionally, the system determines that the performance evaluation value of the fluxgate sensor model is the optimal value, which can be either the maximum value or the minimum value, based on the relationship curve between the performance evaluation value and the characteristic parameters of the fluxgate sensor model and in accordance with preset selection rules, and obtains the characteristic parameters of the candidate magnetic core material corresponding to the performance evaluation value, and uses the candidate magnetic core material as the optimal magnetic core material.

[0074] In the above-mentioned method for determining the core material of the fluxgate, by obtaining the characteristic parameters corresponding to multiple candidate core materials, wherein the characteristic parameters include one or more of the magnetic permeability and magnetic induction intensity, the performance characteristics of different candidate core materials can be understood in many aspects, providing a scientific basis for subsequent material selection. According to the characteristic parameters of each candidate core material and the preset design parameters, multiple fluxgate sensor models are generated; wherein the fluxgate sensor model uses any candidate core material as the core, thereby simulating the performance of each candidate magnetic material in actual application. Using multiple preset excitation voltages, simulation operations are performed on each fluxgate sensor model to determine the relationship curve between the performance evaluation value and the characteristic parameters of each fluxgate sensor model, so as to evaluate the dynamic performance of each candidate core material under different working conditions in actual application. According to the relationship curve between the performance evaluation value and the characteristic parameters of the fluxgate sensor model, the optimal core material is determined from multiple candidate core materials according to the preset selection rules. By analyzing the relationship curve between the performance evaluation value and the characteristic parameters, the key factors affecting the performance of the fluxgate sensor of different candidate core materials can be understood, which provides a quantitative basis for the selection of core materials, reduces the subjectivity of the selection of core materials, and thus improves the adaptability of the selected core materials.

[0075] In an exemplary embodiment, Figure 2 As shown, the performance evaluation value of step S106 includes sensitivity, and a plurality of preset excitation voltages are used to perform simulation operations on each fluxgate sensor model to determine a relationship curve between the performance evaluation value and the characteristic parameters of each fluxgate sensor model, including:

[0076] Step S202 , using a plurality of preset excitation voltages, respectively performs simulation operations on each fluxgate sensor model to obtain a plurality of sets of correlation data between magnetic permeability and sensitivity, and a plurality of sets of correlation data between magnetic induction intensity and sensitivity.

[0077] The sensitivity may generally refer to the response capability of the fluxgate sensor to the change of the magnetic field, that is, the minimum change of the magnetic field that the fluxgate sensor can detect.

[0078] Optionally, the system uses multiple preset excitation voltages so that the fluxgate sensor model is sequentially applied with each preset excitation voltage to perform simulation operations. The output signal of the fluxgate sensor model in the simulation is obtained, and the corresponding magnetic field strength is determined according to each preset excitation voltage, and the sensitivity corresponding to each fluxgate sensor model is calculated according to the output signal of the fluxgate sensor model under each preset excitation voltage. Thus, the correlation data between the characteristic parameters of the magnetic core material constituting each fluxgate sensor model and the sensitivity of the fluxgate sensor model is obtained, that is, multiple sets of correlation data between magnetic permeability and sensitivity, and multiple sets of correlation data between magnetic induction intensity and sensitivity.

[0079] Step S204, generating corresponding first relationship curves and second relationship curves respectively according to the multiple sets of correlation data between magnetic permeability and sensitivity, and the multiple sets of correlation data between magnetic induction intensity and sensitivity.

[0080] The first relationship curve may be a relationship curve diagram between magnetic permeability and sensitivity; the second relationship curve may be a relationship curve diagram between magnetic induction intensity and sensitivity.

[0081] Optionally, the system performs data cleaning operations on multiple sets of correlation data between magnetic permeability and sensitivity, and multiple sets of correlation data between magnetic induction intensity and sensitivity, removes abnormal values ​​and missing values, and after cleaning, performs fitting operations on multiple sets of correlation data between magnetic permeability and sensitivity, and multiple sets of correlation data between magnetic induction intensity and sensitivity using mathematical software or programming language to generate corresponding first relationship curves and second relationship curves. Figure 3As shown, a first relationship curve diagram between permeability and sensitivity is provided, showing the correlation between permeability and sensitivity under different preset excitation voltages. Figure 4 As shown, a second relationship curve diagram between magnetic induction intensity (Bs) and sensitivity (Sensitivity) is provided, showing the correlation between magnetic induction intensity and sensitivity under different preset excitation voltages.

[0082] Step S206: Determine a relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model according to the first relationship curve and the second relationship curve.

[0083] Optionally, the system uses the first relationship curve and the second relationship curve as relationship curves between the performance evaluation value and the characteristic parameter of each fluxgate sensor model.

[0084] In this embodiment, multiple preset excitation voltages are used to simulate each fluxgate sensor, and the correlation data between the characteristic parameters of each candidate magnetic core material and the sensitivity of the fluxgate sensor is obtained, so as to understand the performance of each fluxgate sensor model under different working conditions, thereby improving the accuracy of the design. In addition, by generating the first relationship curve and the second relationship curve, the relationship between the performance of the fluxgate sensor model and the characteristic parameters of the corresponding candidate magnetic core material can be intuitively observed, providing a judgment basis for the subsequent selection of candidate magnetic core materials.

[0085] In an exemplary embodiment, the performance evaluation value in step S206 also includes the output signal strength, and determining the relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model according to the first relationship curve and the second relationship curve includes:

[0086] According to the first relationship curve and the second relationship curve, a third relationship curve between the magnetic induction intensity and the output signal intensity is determined; the first relationship curve, the second relationship curve and the third relationship curve are used as the relationship curves between the performance evaluation value and the characteristic parameters of each fluxgate sensor model.

[0087] The output signal strength may be the peak strength of the second harmonic output signal of the fluxgate sensor and the dynamic test range of the response.

[0088] Optionally, the system determines the saturation excitation voltage according to the first relationship curve and the second relationship curve, and generates a third relationship curve according to the correlation data of the output signal strength and the magnetic induction intensity of the corresponding candidate magnetic core material when the saturation excitation voltage is applied to each fluxgate sensor model. The first relationship curve, the second relationship curve and the third relationship curve are collectively used as the relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model.

[0089] In this embodiment, the first, second and third relationship curves are combined to form a comprehensive performance evaluation framework, which can comprehensively evaluate the performance of each fluxgate sensor model, thereby comparing the advantages and disadvantages of various candidate core materials, providing a scientific basis for selecting suitable core materials, and thus improving the accuracy of the selection.

[0090] In an exemplary embodiment, determining a third relationship curve between magnetic induction intensity and output signal intensity according to the first relationship curve and the second relationship curve includes:

[0091] According to the first relationship curve and the second relationship curve, a preset excitation voltage corresponding to the maximum value of the sensitivity is determined as a saturation excitation voltage; using the saturation excitation voltage, simulation operations are performed on each fluxgate sensor model to obtain multiple sets of correlation data between magnetic induction intensity and output signal intensity; based on the multiple sets of correlation data between magnetic induction intensity and output signal intensity, a third relationship curve is generated.

[0092] The saturation excitation voltage may be an excitation voltage at which the sensitivity of the fluxgate sensor can reach the highest.

[0093] Optionally, the system finds the maximum value of the sensitivity of the fluxgate sensor model based on the first relationship curve and the second relationship curve, and determines the preset excitation voltage value corresponding to the curve where the sensitivity maximum value is located, and uses the preset excitation voltage as the saturation excitation voltage. The saturation excitation voltage is applied to each fluxgate sensor model for simulation operation, and the output signal strength of each fluxgate sensor during the simulation process is collected to obtain multiple sets of correlation data between magnetic induction intensity and signal output intensity. According to the correlation data between the multiple sets of magnetic induction intensity and output signal intensity, a fitting operation is performed to generate a third relationship curve. Figure 5 As shown, a curve diagram showing the relationship between the magnetic induction intensity (Bs) of each candidate core material and the peak intensity (E2) and dynamic test range (H0) of the output signal intensity is provided.

[0094] In this embodiment, by finding the maximum value of the sensitivity and its corresponding excitation voltage, it can be ensured that each fluxgate sensor model is simulated under the optimal excitation voltage, and the relationship between the magnetic induction intensity and the output signal intensity of each fluxgate sensor model under the optimal excitation voltage is further determined to obtain a third relationship curve, which can further provide a basis for the selection of candidate magnetic core materials, thereby improving the adaptability of the selected magnetic core material.

[0095] In an exemplary embodiment, step S108 determines the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model and a preset selection rule, including:

[0096] According to the relationship curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model, the characteristic parameter corresponding to the maximum value of the performance evaluation value of the fluxgate sensor model is selected; and the candidate magnetic core material corresponding to the characteristic parameter is used as the optimal magnetic core material.

[0097] The optimal magnetic core material may be a candidate magnetic core material that can enable the performance of the fluxgate sensor to reach an optimal state.

[0098] Optionally, the system determines the maximum value of the performance evaluation value according to the relationship curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model, and Figure 4 It can be seen that the change of magnetic induction intensity has little effect on the maximum sensitivity, but according to Figure 3 and Figure 5 It can be seen that the magnetic permeability is proportional to the sensitivity, and the magnetic induction intensity is proportional to the output signal intensity. Therefore, the characteristic parameters corresponding to the maximum value of the performance evaluation value are determined, such as determining the optimal magnetic permeability corresponding to the maximum sensitivity, and the optimal magnetic induction intensity corresponding to the maximum output signal intensity. The candidate magnetic core material whose magnetic permeability and magnetic induction intensity included in the characteristic parameters are closest to the optimal magnetic permeability and the optimal magnetic induction intensity is determined as the optimal magnetic core material.

[0099] In this embodiment, by finding the maximum value of the performance evaluation value of the fluxgate sensor model and its corresponding characteristic parameters, and determining the candidate core material closest to the characteristic parameters from multiple candidate core materials as the optimal core material, the core material that can ensure the maximum performance of the fluxgate sensor is selected, thereby improving the adaptability of the selected core material.

[0100] In an exemplary embodiment, step S104 generates multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters, including:

[0101] According to the magnetic permeability of each candidate magnetic core material, the magnetic flux density of each candidate magnetic core material is determined; according to the magnetic flux density of each candidate magnetic core material, the magnetic core cross-sectional area of ​​each fluxgate sensor model is determined; according to the preset design parameters, the characteristic parameters of each candidate magnetic core material and the magnetic core cross-sectional area of ​​each fluxgate sensor model, multiple fluxgate sensor models are generated.

[0102] Among them, magnetic flux density can be a physical quantity that describes the strength of the magnetic field, usually represented by the symbol B. It is defined as the magnetic flux passing through a unit area, and the unit is Tesla (symbol T). Magnetic flux density reflects the strength and direction of the magnetic field, and is usually related to the magnetic field described in electromagnetism.

[0103] The core cross-sectional area may be the cross-sectional area of ​​the core of the fluxgate sensor.

[0104] Optionally, the system calculates the magnetic flux density of each candidate core material based on the magnetic permeability of each candidate core material and the magnetic field strength under each excitation voltage, and determines the core cross-sectional area of ​​each fluxgate sensor model based on the magnetic flux density and the magnetic flux of each candidate core material obtained under each excitation voltage. The system inputs the preset design parameters including the shape of the core, coil length, number of coil turns, etc., the characteristic parameters of each candidate core material, and the calculated core cross-sectional area into the modeling software to generate multiple fluxgate sensor models. Among them, the magnetic flux of the candidate core material can be measured by a fluxmeter or by applying an external magnetic field to measure the response of the candidate core material.

[0105] In this embodiment, all candidate magnetic core materials and their characteristic parameters are input into the modeling software to generate multiple fluxgate sensor models, which can effectively evaluate the performance of different combinations of fluxgate sensor models, providing more options for the subsequent design of fluxgate sensors, thereby optimizing the design of fluxgate sensors.

[0106] In one embodiment, Figure 6 As shown, a method for analyzing the characteristics of fluxgate core materials is provided, and the specific steps include:

[0107] Step 1: Initialize the test environment of the candidate magnetic core material.

[0108] Step 2: determine the magnetic induction intensity Bs and magnetic permeability of the soft magnetic material to be tested (corresponding to the candidate magnetic core material of the above embodiment).

[0109] Step 3: Set different excitation voltage amplitudes according to experimental requirements.

[0110] Step 4, test the relationship between sensitivity and Bs: Under the set excitation voltage, test the influence of magnetic core materials with different Bs values ​​on sensor sensitivity. Record the data and draw a "curve of change between sensitivity and Bs".

[0111] Step 5, test the relationship between the second harmonic output signal strength and Bs: Under saturation excitation conditions, test the influence of magnetic core materials with different Bs values ​​on the sensor output signal. Record the data and analyze the "influence of Bs value on output signal".

[0112] Step 6, test the relationship between sensitivity and magnetic permeability: Under different excitation conditions, test the effect of the magnetic permeability of the core material on the sensitivity of the sensor. Record the data and draw a "curve of the effect of magnetic permeability on sensitivity".

[0113] Step 7, compare the sensor performance under different Bs values ​​and magnetic permeabilities to determine the optimal core material property group.

[0114] In this embodiment, the influence of several main characteristic parameters of soft magnetic materials on the performance of fluxgate sensor is studied, and on this basis, simulation analysis is performed through specific material parameters, and the performance of sensors of several common magnetic core materials is compared. Using suitable soft magnetic film materials is often the most convenient and effective way to improve sensor performance. After simulation analysis of the characteristics of many soft magnetic film materials, Permalloy and amorphous and nanocrystalline materials have become the main selection objects of current fluxgate core materials due to their unique high magnetic permeability characteristics, ensuring that the fluxgate sensor has excellent sensitivity, accuracy, response time and high stability.

[0115] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the core material of a fluxgate for implementing the method for determining the core material of a fluxgate involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for determining the core material of a fluxgate provided below can refer to the limitations of the method for determining the core material of a fluxgate above, and will not be repeated here.

[0117] In an exemplary embodiment, Figure 7 As shown, a device 700 for determining a magnetic core material of a fluxgate is provided, comprising: a parameter acquisition module 702, a model generation module 704, a curve determination module 706 and a material determination module 708, wherein:

[0118] The parameter acquisition module 702 is used to acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity.

[0119] The model generation module 704 is used to generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any candidate magnetic core material as the magnetic core.

[0120] The curve determination module 706 is used to use a plurality of preset excitation voltages to perform simulation operations on each fluxgate sensor model to determine a relationship curve between the performance evaluation value and the characteristic parameters of each fluxgate sensor model.

[0121] The material determination module 708 is used to determine the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value and the characteristic parameter of the fluxgate sensor model and a preset selection rule.

[0122] Furthermore, in one embodiment, the curve determination module 706 is also used to use multiple preset excitation voltages to perform simulation operations on each fluxgate sensor model, respectively, to obtain multiple sets of correlation data between magnetic permeability and sensitivity, and multiple sets of correlation data between magnetic induction intensity and sensitivity; based on the multiple sets of correlation data between magnetic permeability and sensitivity, and the multiple sets of correlation data between magnetic induction intensity and sensitivity, respectively generate corresponding first relationship curves and second relationship curves; based on the first relationship curve and the second relationship curve, determine the relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model.

[0123] Furthermore, in one embodiment, the curve determination module 706 is also used to determine a third relationship curve between the magnetic induction intensity and the output signal intensity based on the first relationship curve and the second relationship curve; and use the first relationship curve, the second relationship curve and the third relationship curve as relationship curves between the performance evaluation value and the characteristic parameters of each fluxgate sensor model.

[0124] Furthermore, in one embodiment, the curve determination module 706 is also used to determine a preset excitation voltage corresponding to the maximum value of the sensitivity as a saturation excitation voltage based on the first relationship curve and the second relationship curve; using the saturation excitation voltage, respectively perform simulation operations on each fluxgate sensor model to obtain multiple sets of correlation data between magnetic induction intensity and output signal intensity; and generate a third relationship curve based on the multiple sets of correlation data between magnetic induction intensity and output signal intensity.

[0125] Furthermore, in one embodiment, the material determination module 708 is also used to select the characteristic parameter corresponding to the maximum value of the performance evaluation value of the fluxgate sensor model based on the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter; and select the candidate core material corresponding to the characteristic parameter as the optimal core material.

[0126] Furthermore, in one embodiment, the model generation module 704 is also used to determine the magnetic flux density of each candidate magnetic core material based on the magnetic permeability of each candidate magnetic core material; determine the core cross-sectional area of ​​each fluxgate sensor model based on the magnetic flux density of each candidate magnetic core material; and generate multiple fluxgate sensor models based on preset design parameters, characteristic parameters of each candidate magnetic core material and the core cross-sectional area of ​​each fluxgate sensor model.

[0127] Each module in the above-mentioned fluxgate core material determination device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0128] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store characteristic parameter data of candidate magnetic core materials. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the magnetic core material of a fluxgate is implemented.

[0129] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0130] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0132] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0133] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0134] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0135] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining the core material of a fluxgate, characterized in that: The method comprises: Acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity; Generate multiple fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and the preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as the magnetic core; Using a plurality of preset excitation voltages, a simulation operation is performed on each of the fluxgate sensor models to determine a relationship curve between a performance evaluation value of each of the fluxgate sensor models and the characteristic parameter; According to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, the optimal magnetic core material is determined from the plurality of candidate magnetic core materials according to a preset selection rule.

2. The method according to claim 1, characterized in that The performance evaluation value includes sensitivity. The simulation operation is performed on each fluxgate sensor model using a plurality of preset excitation voltages to determine a relationship curve between the performance evaluation value of each fluxgate sensor model and the characteristic parameter, including: Using a plurality of preset excitation voltages, respectively, a simulation operation is performed on each fluxgate sensor model to obtain a plurality of sets of correlation data between the magnetic permeability and the sensitivity, and a plurality of sets of correlation data between the magnetic induction intensity and the sensitivity; Generate corresponding first relationship curves and second relationship curves respectively according to multiple sets of correlation data between the magnetic permeability and the sensitivity, and multiple sets of correlation data between the magnetic induction intensity and the sensitivity; A relationship curve between the performance evaluation value and the characteristic parameter of each fluxgate sensor model is determined according to the first relationship curve and the second relationship curve.

3. The method according to claim 2, characterized in that The performance evaluation value also includes output signal strength, and determining the relationship curve between the performance evaluation value of each fluxgate sensor model and the characteristic parameter according to the first relationship curve and the second relationship curve includes: Determining a third relationship curve between the magnetic induction intensity and the output signal intensity according to the first relationship curve and the second relationship curve; The first relationship curve, the second relationship curve and the third relationship curve are used as relationship curves between the performance evaluation value and the characteristic parameter of each fluxgate sensor model.

4. The method according to claim 3, characterized in that The determining, based on the first relationship curve and the second relationship curve, a third relationship curve between the magnetic induction intensity and the output signal intensity comprises: Determine the preset excitation voltage corresponding to the maximum value of the sensitivity according to the first relationship curve and the second relationship curve as a saturation excitation voltage; Using the saturation excitation voltage, respectively perform simulation operations on each fluxgate sensor model to obtain multiple groups of correlation data between the magnetic induction intensity and the output signal intensity; A third relationship curve is generated according to a plurality of sets of correlation data between the magnetic induction intensity and the output signal intensity.

5. The method according to claim 1, characterized in that The step of determining the optimal magnetic core material from the plurality of candidate magnetic core materials according to the relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter and according to a preset selection rule comprises: According to a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter, selecting the characteristic parameter corresponding to the maximum value of the performance evaluation value of the fluxgate sensor model; The candidate magnetic core material corresponding to the characteristic parameter is used as the optimal magnetic core material.

6. The method according to claim 1, characterized in that The step of generating a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters comprises: Determining the magnetic flux density of each candidate magnetic core material according to the magnetic permeability of each candidate magnetic core material; Determining the core cross-sectional area of ​​each fluxgate sensor model according to the magnetic flux density of each candidate magnetic core material; A plurality of fluxgate sensor models are generated according to the preset design parameters, the characteristic parameters of each candidate magnetic core material and the magnetic core cross-sectional area of ​​each fluxgate sensor model.

7. A device for determining the magnetic core material of a fluxgate, characterized in that: The device comprises: A parameter acquisition module, used to acquire characteristic parameters corresponding to a plurality of candidate magnetic core materials; the characteristic parameters include one or more of magnetic permeability and magnetic induction intensity; A model generation module, used to generate a plurality of fluxgate sensor models according to the characteristic parameters of each candidate magnetic core material and preset design parameters; the fluxgate sensor model uses any of the candidate magnetic core materials as a magnetic core; A curve determination module, used to use a plurality of preset excitation voltages to perform simulation operations on each of the fluxgate sensor models, and determine a relationship curve between the performance evaluation value of each of the fluxgate sensor models and the characteristic parameters; The material determination module is used to determine the optimal magnetic core material from a plurality of candidate magnetic core materials according to a relationship curve between the performance evaluation value of the fluxgate sensor model and the characteristic parameter and according to a preset selection rule.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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